Use of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: A practical alternative to hospital-based follow-up for breast cancer clinical trials

Use of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: A practical alternative to hospital-based follow-up for breast cancer clinical trials | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Use of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: A practical alternative to hospital-based follow-up for breast cancer clinical trials Lucy Suzanne Kilburn, Victoria Hinder, Sikhuphukile Gillian Ndebele-Mahati, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4780757/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Trials → Version 1 posted 5 You are reading this latest preprint version Abstract Background : With continued improvements in breast cancer (BC) outcomes and risk of recurrence occurring until at least 20 years post-diagnosis, it is important to continue to follow-up clinical trial participants to characterise long-term treatment impact. Traditionally follow-up has been via hospitals; entailing burden on patients and site-staff. Using routinely collected health datasets (RCHD) as an alternative method is attractive, but historically cancer recurrence is poorly recorded unlike initial cancer diagnosis. Here we use data collected prospectively from large, multi-centre BC clinical trials to develop and test a procedure to identify recurrence within RCHD. Methods : Data from four trials of early breast cancer (TACT2, POETIC, IMPORT-HIGH and FAST-Forward) where recurrence data has been collected prospectively (gold standard) was linked with RCHD (incl. cancer registry and hospital episode statistics; HES) managed by NHS England. The procedure identified episodes of clinical activity within RCHD to classify each event type (local and distant recurrence, second cancers, death) separately then combined to derive time-to-recurrence (TTR), disease-free survival (iDFS) and overall survival (OS) outcomes. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Hazard ratios using Cox regression modelling, log rank test p-values and three-year survival-rates for the randomised treatments were reported separately for RCHD and trial data. Results: The final procedure used Cancer Registry diagnoses to identify initial BCs for quality control purposes and second primary cancers. Deaths were identified via death dates and cause. Distant recurrence was identified predominately by direct indicators of metastases (e.g. ICD10 codes C77X-79X). Local recurrence was identified via relevant surgeries’ OPCS4 codes. For TTR, iDFS and OS, agreement between study and RCHD events was reasonable. Specificity was good across all endpoints (range:97.9%-99.9% for three training datasets combined), as was NPV (range:95.2%-99.6%). Sensitivity and PPV were more variable with sensitivity ranging between 72.9%-97.2% and PPV ranging between 82.6%-99.5%. Values were similar when considering the test dataset. Survival estimates for TTR, iDFS and OS were similar between study and RCHD data. Conclusion: It is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis. cancer trials routine data linkage recurrence Figures Figure 1 Figure 2 Introduction Breast cancer is the most common cancer affecting women in the UK with over 55,000 cases per year. Incidence data between 2013–2017 show that over 85% of women survive for five years or more and 76% survive for 10 years or more, with survival rates doubling over the last 40 years( 1 ). This is encouraging news for patients, but it also means that participants in breast cancer clinical trials need to be followed-up for longer than the typical 3 or 5-year analysis timepoint to fully establish the long-term impact of treatments under investigation. For example, recurrence risk for hormone sensitive breast cancer extends beyond 20 years with the impact of benefits and risks of adjuvant treatment detectable throughout that follow-up period( 2 , 3 ). Randomised controlled trials (RCTs) are recognised to be the optimal scientifically rigorous method for hypothesis testing and considered the gold standard approach to evaluate the effectiveness of a treatment intervention. However, long-term assessment of both disease-related outcomes and treatment-related sequelae can be challenging for trialists given the effort required for data collection and comes at a considerable cost to research funders and participating NHS provider sites; a cost many funders are often no longer willing to bare. Coupled with competing pressures within the clinical setting, where oncology patients are routinely discharged early from hospital-based follow-up and NIHR CRN is resource constrained, there is an increased threat to researchers’ ability to collect the requisite follow-up information. In an era which foresees greater integration of data, there is an increasing resolve within the clinical trials community to minimise data collected via completion of case report forms (CRFs) and to advocate the use of routinely collected data sources where possible. This will be of considerable importance going forward, given the broad recognition that clinically important consequences following breast cancer treatment, such as impact on cardiovascular disease, may only become detectable > 10 years after treatment delivery( 4 ). The immediate challenge in the UK however is that in spite of inclusion in the Cancer Outcomes and Services Dataset (COSD) cancer recurrences, per se, are not universally collected in a single identifiable data field as is established for cancer diagnoses. This leaves a need for derivation of procedures capable of interrogating multiple datasets and extracting patterns of activity in health data which predict for a cancer recurrence. Clinical trials which have mature follow-up for disease-related outcomes provide excellent vehicles for validation of such procedures including estimation of their positive and negative predictive value. UK routinely collected healthcare data (RCHD) has already been shown to be a reliable source of information in terms of treatment delivery and overall survival (OS) in bladder cancer( 5 ) and, specifically, an evaluation of data provenance and integrity of hospital episode statistics and death outcomes has shown that these datasets have robust curation equivalent to high quality source data, therefore can be considered sufficiently reliable for use in clinical trials( 6 ). However, uptake of use of RCHD within clinical trials remains low with an estimated 3% of data releases from UK registries between 2013–2018 being linked to clinical trials, despite it seeming to be a sensible alternative for data collection given the burden hospital-based CRF completion places on the NHS( 7 ). One of the challenges with using RCHD in cancer clinical trials however and a major reason for its low usage to date, is the lack of systematically collected information relating to recurrence following a cancer diagnosis. Disease-related endpoints used within breast cancer trials, such as invasive disease-free survival (iDFS; 8), are composite endpoints formed of multiple types of disease-related event, namely for iDFS; local recurrence, distant recurrence, new second primary cancer and death. Primary breast cancer diagnosis and any subsequent second primaries are recorded as new diagnoses within cancer registry datasets; however, local or distant recurrences linked to the primary breast cancer are not reliably or consistently collected within currently available RCHD. Working on the premise that upon diagnosis of recurrence a large proportion of breast cancer patients will go on to receive further treatment as part of standard-of-care, we aimed to identify a procedure to identify recurrences within RCHD. We wanted to explore whether key episodes or events within the RCHD datasets following a patient’s initial breast cancer diagnosis could be used as reliable indicators that the cancer had returned. In order to establish validity of any such derivation comparison against a reliable source is required. We postulate that data collected prospectively for the purposes of large, multi-centre academically sponsored RCTs with mature clinical data on disease-related outcomes form such a gold-standard and have thus linked data from four UK academically sponsored breast cancer trials. This work builds upon a previous project, comparing an older subset of linked cancer registration data (held in the National Cancer Data Repository (NCDR)) and data from the TACT breast cancer trial, that established a high degree of matching between NCDR and trial data( 9 ). Methods Four early breast cancer trials were chosen for this project to provide a combined “gold standard” cohort against which an RCHD derivation of a subsequent breast cancer disease related event could be compared. These were (POETIC (ISRCTN63882543), IMPORT HIGH (ISRCTN18654225), TACT2 (ISRCTN68068041) and FAST-Forward (ISRCTN19906132)) managed by The Institute of Cancer Research Clinical Trials & Statistics Unit (ICR-CTSU) with English RCHD managed by the National Cancer Registry and Analysis Service (NCRAS), formally within Public Health England (PHE), now part of NHS England. Trials had mature follow-up (median 60.9, 42.0, 108.4 and 41.2 months for the 4 trials above respectively) and collectively included over 13,324 patients who had been treated at hospitals across England. Relevant participants from the four trials were identified by ICR-CTSU in accordance with the following inclusion criteria: that the patient had histologically confirmed breast cancer, the patient had valid consent to link to medical records/access medical records and the patient had a valid NHS number within England and Wales to allow for transfers between countries (i.e. excluding patients with CHI numbers). NCRAS performed deterministic matching to the following datasets: Cancer Registry (including vital status) and Cancer Outcomes and Services Dataset (COSD), Hospital Episode Statistics – admitted patient care (HES APC), – outpatients (HES OP) and – accident and emergency (HES A&E), Radiotherapy Dataset (RTDS) and Systemic Anti-Cancer Therapy Dataset (SACT). Patients were considered correctly matched if their date of birth was the same in the Cancer Registry and trial datasets. Development of a disease-related event identification procedure within RCHD was via identification of key events within a patient’s pathway that were considered highly-likely to be indicative of a recurrence. Events identified via this procedure would then be used as a proxy for confirmation of recurrence minimising use of known patient specific information (e.g. avoiding use of exact treatment details) to make it as generalizable as possible whilst maximising accuracy. The first aim was to identify the initial breast cancer diagnosis, second primary cancers and deaths given their anticipated ease of ascertainment as RCHD registry landmark events. How recurrence would be identified was less obvious, but the initial plan was to look for activity related to the management of the cancer. E.g. biopsies, scans, delivery of cancer treatment. Once each individual event was identified they would be compared to the trial data and adjustments made to the process. The procedure was developed (trained) on POETIC trial data initially, then tested and refined using TACT2 and IMPORT HIGH data. In an iterative approach, the development of the procedure involved initial identification of disease-related events conducted blind to the trial outcome data and then the procedure was refined if improvements could be made after initial matching. FAST-Forward data was used as a separate internal validation of the full working disease-related event identification procedure, thus all recurrence, second primary and death events were identified first within the RCHD blinded to trial outcome data then compared to the trial outcome data as the final step. At this point mis-classifications between the RCHD and trial data were assessed, for example, trial data recording an event as a distant recurrence but recorded as a second primary within RCHD. In addition, consideration was given to whether any of the components could be improved by knowledge of another, e.g. information on the death indicates metastatic relapse. To ensure a fair comparison to assess how well the routine data identification matched to the trial data, patients were censored at the date of their last follow-up available from the HES datasets or the patient’s last trial follow-up, whichever was the earliest. However, when the endpoint was OS the last follow-up date available from the Cancer Registry dataset was used as death data is updated within NCRAS more frequently. Time in follow-up between RCHD datasets and trial data was explored using reverse Kaplan Meier and the mean difference and 95% CI were reported. For the purpose of classification of a match between clinical trial and RCHD, all event dates (recurrence, second primary cancer or death) within 12 weeks of each other were classed as agreement. Where the number of events allowed for each event-type, measures of agreement including sensitivity and specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to assess matching between routine data and trial data for linked patients for each of the four trials separately and for the three test trials combined. Scatter plots were used to visualise closeness of matches and possible reasons for an event but no match; for example, different classifications of an event or where an event was close to the last known follow-up date for a given dataset. In addition, estimations of the differences between dates when events were identified in the NCRAS datasets versus the trial data were reported via mean and 95%CIs. In addition, once the disease-related event identification procedure was completed for each trial, survival-related outcomes relevant to long term follow-up of time to recurrence (TTR equivalent to Relapse Free Interval (RFI) in STEEP2( 8 )- defined as time from randomisation to local, regional, or distant tumour recurrence or death from breast cancer without prior notification of relapse. Second primary cancers and intercurrent deaths were treated as censoring events. Patients who were alive and disease free were censored at the date last seen alive), iDFS (defined as time from randomisation until first confirmed relapse of this breast cancer, new second primary or death from any cause)( 8 ) and OS were calculated using the RCHD and separately using clinical trial data for eligible, linked patients only. Survival-related endpoints were presented using Kaplan Meier plots. Hazard ratios (HR) using Cox regression modelling, log rank test p-values and three-year survival-rates for the randomised treatments were reported separately for RCHD and trial data. For TACT2, the trial with the longest follow up, seven-year estimates were also reported to show estimates in the long-term follow-up setting. Levels of agreement and estimates of treatment effect were used to assess whether routinely RCHD can be used as an alternative to hospital-based follow-up in patients participating in breast cancer trials. Data processing and analyses were conducted using Stata version 16.1 and latterly, version 17. Results NCRAS linked RCHD datasets for the four clinical trials were received by ICR-CTSU between January-April 2018. These included tumour data up to 31/12/2015, HES data to 31/01/2016 and death data to 31/12/2016. SACT data and RTDS data were reported up to 28/02/2017 and 31/03/2016 respectively. For 3 of the 4 trials (POETIC, FAST-Forward and IMPORT HIGH) data snapshots where those used previously for the primary publication of the clinical trial results( 10 – 12 ). For the fourth (TACT2), where publication of results been based on an earlier data snapshot( 13 ), the snapshot taken for the primary analysis was initially used but later surpassed with an updated, unpublished dataset to include the additional time period of data received by NCRAS. The majority of NHS numbers that were sent to NCRAS were successfully linked (overall 93%). Data from patients resident in Wales who had some treatment at English hospitals post-diagnosis were not included in the analysis due to sporadic availability of NCRAS data. Overall, based on the trial datasets 524 (3.9%) patients were classed as lost to follow-up within the four trials based on information from hospital staff (Figure A1). Procedure development Initial breast cancer diagnoses were identified via a new cancer diagnosis in the Cancer Registry dataset and the distribution of the dates compared to the trial data were reviewed to establish whether they should be considered related to the initial diagnosis. Date of surgery for removal of primary disease and date of randomisation were used as surrogates for date of diagnosis in the trial datasets. After reviewing the data, a window of up to 12 weeks post-randomisation or surgery for removal of primary in the absence of diagnosis date within the trial data was deemed acceptable to classify as due to the initial diagnosis. Fields used to identify breast and non-breast cancer diagnoses are included in Fig. 1 . Identifying date of death was straightforward within routine datasets via a single field within the Cancer Registry dataset. Identifying whether the cause of death was breast cancer related, in particular whether the patient had metastatic disease or not, was established using the cause of death fields available (see supplementary material for further details). Second primary cancers were identified from the new cancer diagnosis field in the Cancer Registry dataset. Only invasive cancers were relevant in this setting for the trials selected and invasive status was determined by morphology and behaviour ICD codes and sites of disease (e.g. skin basal cell carcinomas were counted as non-invasive and therefore not counted as an event) Disease recurrence was identified via looking for cancer treatment activities in the cancer registry treatment dataset, HES OP, HES APC, RTDS and SACT datasets. Exploration of this involved two methods to identify metastatic disease that were later rejected (see supplementary materials for details) before identifying recurrence by using the first occurrence of the ICD-10 diagnosis codes C77X (excluding C773 codes as these represent cancer in axillary nodes), C78X and C79X within the HES OP and HES APC diagnosis fields that are provided at each hospital visit. These ICD-10 codes do not indicate the primary cancer diagnosis, it was therefore assumed that the metastatic disease was for breast cancer unless the patient had a non-breast second primary cancer diagnosed before or within 8 weeks of the metastatic diagnosis. In this case the metastatic disease was attributed to the second primary cancer. The RTDS has a field that indicates the intent (curative, palliative) of the radiotherapy; therefore, this was used to provide an additional check on a patient’s metastatic disease status. In addition, the SACT dataset was reviewed for the type of treatment and assigned into the categories “adjuvant”, “metastatic”; providing an additional check on a patient’s metastatic status. Additional RTDS and SACT data fields such as specific treatment details were reviewed and included as part of the initial procedure development, but for simplicity, with the risk of introducing a higher rate of false positives, and desire for a more generalisable, future-proof procedure they were not formally included in the final procedure. In addition, where cause of death included metastatic breast cancer this was used to identify metastatic disease if not picked up in the HES data. Determining local recurrence was the most problematic as it was not easy to distinguish events due to the patient’s initial disease and subsequent recurrence. The ICD-10 code C50X represents a breast cancer diagnosis, however it may be used also when a patient is having ongoing treatment for cancer regardless as to whether the cancer is still present. Therefore C50X codes alone could not be used to determine local recurrence. Codes representing breast and axillary lymph node excision within HES and Cancer Registry were used to identify possible treatment for local recurrence, providing the events happened 12 months after diagnosis (to avoid identifying the main local treatment normally given for primary disease). Considering these surgery codes on their own in HES, without a C50X code attached to the episode was not sufficient as a considerable amount of false positives were identified. Therefore, the patient had to have a C50X code at the same visit to better identify local recurrent events. Once the data needed to identify each event had been established a ‘final’ set of programs was developed to streamline the procedure. The process map for disease-related event identification is summarised in Fig. 1 . Performance of procedure A total of 1008 TTR events, 1547 iDFS events and 1124 deaths from a total of 9744 patients were available for detection from the three training trials combined; of which 917 (91.0%), 1460 (94.4%) and 1095 (97.4%) events respectively were correctly identified within RCHD. For the three key disease-related outcomes, agreement between trial events and NCRAS events was moderately good. Specificity was good across all endpoints (range: 97.9%-99.9% for three trials combined), as was NPV (range: 99.0%-99.7%), highlighting the low number of false positives from RCHD. Sensitivity and PPV were more variable across the trials with sensitivity ranging between 91.0%-97.4% for events detected at any timepoint and PPV ranging between 85.5%-99.5%. In all cases, these values were reduced when considering the validation dataset (FAST-Forward); however, the number of events available for detection for this trial was low. Similarly, sensitivity and PPV were reduced for all endpoints when considering events identified within a window of +/-26 weeks; a window that was considered appropriate in the context of long-term follow-up setting when contact with patients is often on an annual or bi-annual basis; this is broken down further to a window of +/- 12 weeks where the majority of events were identified (Table 1 ). The variability in agreement measures, particularly sensitivity and PPV continues when considering the component events that individually make up TTR and iDFS with the poorest agreement found when trying to identify loco-regional recurrences (Table 2 ). Table 1: Level of agreement between NCRAS and trial data for each survival-related outcome TTR POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 249 57 306 112 16 128 556 82 638 917 155 1072 102 23 125 Event within 12 wks 186 0 186 79 0 79 377 0 377 642 0 642 87 0 87 Event within 12-26 wks 18 0 18 16 0 16 59 0 59 93 0 93 11 0 11 Event > 26 weeks 45 0 45 17 0 17 120 0 120 182 0 182 4 0 4 No event 29 3,670 3699 14 2387 2401 48 2524 2572 91 8581 8672 17 3435 3452 Total 278 3,727 4005 126 2403 2529 604 2606 3210 1008 8736 9744 119 3458 3577 Accuracy – overall Sensitivity 89.6% PPV 81.4% 88.9% PPV 87.5% 92.1% PPV 87.1% 91.0% PPV 85.5% 85.7% PPV 81.6% 95%CI (85.4-92.9) (76.6-85.6) (82.1-93.8) (80.5-92.7) (89.6-94.1) (84.3-89.6) (89.0-92.7) (83.3-87.6) (78.1-91.5) (73.7-88.0) Specificity 98.5% NPV 99.2% 99.3% NPV 99.4% 96.9% NPV 98.1% 98.2% NPV 99.0% 99.3% NPV 99.5% 95%CI (98.0-98.8) (98.9-99.5) (98.9-99.6) (99.0-99.7) (96.1-97.5) (97.5-98.6) (97.9-98.5) (98.7-99.2) (99.0-99.6) (99.2-99.7) Difference in event dates, wks (95% CI) -8.7 (-13.9 to -3.6) -9.4 (-14.1 to-4.8) -19.2 (-23.2 to -15.1) -15.1 (-18.0 to -12.3) -3.3 (-5.3 to -1.3) Accuracy - within 26 weeks Sensitivity 73.4% PPV 78.2% 75.4% PPV 85.6% 72.2% PPV 84.2% 72.9% PPV 82.6% 82.4% PPV 81.0% 95%CI (67.8-78.5) (72.7-83.0) (66.9-82.6) (77.6-91.5) (68.4-75.7) (80.7-87.2) (70.1-75.6) (79.9-85.0) (74.3-88.7) (72.9-87.6) Specificity 98.5% NPV 98.0% 99.3% NPV 98.7% 96.9% NPV 93.8% 98.2% NPV 96.9% 99.3% NPV 99.4% 95%CI (98.0-98.8) (97.5-98.4) (98.9-99.6) (98.2-99.1) (96.1-97.5) (92.8-94.6) (97.9-98.5) (96.5-97.3) (99.0-99.6) (99.1-99.6) iDFS POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 543 61 604 158 13 171 759 96 855 1460 170 1630 194 32 226 Event within 12 wks 456 0 456 121 0 121 554 0 554 1131 0 1131 173 0 173 Event within 12-26 wks 26 0 26 17 0 17 67 0 67 110 0 110 13 0 13 Event > 26 wks 61 0 61 20 0 20 138 0 138 219 0 219 8 0 8 No event 35 3366 3401 14 2344 2358 38 2317 2355 87 8027 8114 21 3330 3351 Total 578 3427 4005 172 2357 2529 797 2413 3210 1547 8197 9744 215 3362 3577 Accuracy - overall Sensitivity 93.9% PPV 89.9% 91.9% PPV 92.4% 95.2% PPV 88.8% 94.4% PPV 89.6% 90.2% PPV 85.8% 95%CI (91.7-95.7) (87.2-92.2) (86.7-95.5) (87.4-95.9) (93.5-96.6) (86.5-90.8) (93.1-95.5) (88.0-91.0) (85.5-93.9) (80.6-90.1) Specificity 98.2% NPV 99.0% 99.4% NPV 99.4% 96.0% NPV 98.4% 97.9% NPV 98.9% 99.0% NPV 99.4% 95%CI (97.7-98.6) (98.6-99.3) (99.1-99.7) (99.0-99.7) (95.2-96.8) (97.8-98.9) (97.6-98.2) (98.7-99.1) (98.7-99.3) ((99.0-99.6) Difference in event dates, wks (95% CI) -2.2 (-5.1 to -0.6) -6.3 (-9.9 to -2.7) -13.6 (-16.8 to -10.4) -8.6 (-10.6 to -6.5) -1.3 (-2.8 to 0.2) Accuracy - within 26 weeks Sensitivity 83.4% PPV 88.8% 80.2% PPV 91.4% 77.9% PPV 86.6% 80.2% PPV 88.0% 86.5% PPV 85.3% 95%CI (80.1-86.3) (85.8-91.3) (73.5-85.9) (85.7-95.3) (74.9-80.8) (83.9-89.0) (78.1-82.2) (86.1-89.6) (81.2-90.8) (79.9-89.7) Specificity 98.2% NPV 97.2% 99.4% NPV 98.6% 96.0% NPV 92.9% 97.9% NPV 96.3% 99.0% NPV 99.1% 95%CI (97.7-98.6) (96.6-97.7) (99.1-99.7) (98.0-99.0) (95.2-96.8) (91.9-93.9) (97.6-98.2) (95.9-96.7) (98.7-99.3) (98.8-99.4) Table 1: Level of agreement between NCRAS and trial data for each survival-related outcome (continued) OS POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 429 2 431 116 0 116 550 4 554 1095 6 1101 137 0 137 Event within 12 wks 428 0 428 116 0 116 548 0 548 1092 0 1092 136 0 136 Event within 12-26 wks 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 Event > 26 wks 1 0 1 0 0 0 1 0 1 2 0 2 1 0 1 No event 17 3557 3574 5 2408 2413 7 2649 2656 29 8614 8643 9 3431 3440 Total 446 3559 4005 121 2408 2529 557 2653 3210 1124 8620 9744 146 3431 3577 Accuracy - overall Sensitivity 96.2% PPV 99.5% 95.9% PPV 100.0% 98.7% PPV 99.3% 97.4% PPV 99.5% 93.8% PPV 100.0% 95%CI (94.0-97.8) (98.3-99.9) (90.6-98.6) (96.9-100.0) (97.4-99.5) (98.2-99.8) (96.3-98.3) (98.8-99.8) (88.6-97.1) (97.3-100.0) Specificity 99.9% NPV 99.5% 100.0% NPV 99.8% 99.8% NPV 99.7% 99.9% NPV 99.7% 100.0% NPV 99.7% 95%CI (99.8-100.0) (99.2-99.7) (99.8-100.0) (99.5-99.9) (99.6-100.0) (99.5-99.9) (99.8-100.0) (99.5-99.8) (99.9-100.0) (99.5-99.9) Difference in event dates, wks (95% CI) 0.2 (-0.1 to 0.4) p 0.1 (-0.0 to 0.1) I 0.1 (-0.1 to 0.3) T 0.1 (-0.0 to 0.3) 0.8 (-0.7 to 2.3) F Accuracy - within 26 weeks Sensitivity 96.0% PPV 99.5% 95.9% PPV 100.0% 98.6% PPV 99.3% 97.2% PPV 99.5% 93.2% PPV 100.0% 95%CI (93.7-97.6) (98.3-99.9) (90.6-98.6) (96.9-100.0) (97.2-99.4) (98.2-99.8) (96.1-98.1) (98.8-99.8) (87.8-96.7) (97.3-100.0) Specificity 99.9% NPV 99.5% 100.0% NPV 99.8% 99.8% NPV 99.7% 99.9% NPV 99.6% 100.0% NPV 99.7% 95%CI (99.8-100.0) (99.2-99.7) (99.8-100.0) (99.5-99.9) (99.6-100.0) (99.4-99.9) (99.8-100.0) (99.5-99.8) (99.9-100.0) (99.5-99.9) P POETIC: Date of death doesn't match n=30, 26 where two out of day, month and year match, 4 where only one out of day, month, year match. I IMPORT HIGH: Date of death doesn't match n=10, 9 where two out of day, month and year match, 1 where none out of day, month, year match. T TACT2: Date of death doesn't match n=32, 22 where two out of day, month and year match, 9 where only one out of day, month, year match, 1 where none out of day, month, year match. F FAST-Forward: Date of death doesn't match n=11, 10 where two out of day, month and year match, 1 where only one out of day, month, year match Table 2: Level of agreement between NCRAS and trial data for each event type Distant recurrence POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 240 35 275 93 15 108 499 60 559 832 110 942 88 18 106 Event within 12 wks 173 0 173 68 0 68 329 0 329 570 0 570 70 0 70 Event within 12-26 wks 24 0 24 14 0 14 63 0 63 101 0 101 11 0 11 Event > 26 wks 43 0 43 11 0 11 107 0 107 161 0 161 7 0 7 No event 23 3707 3730 11 2410 2421 42 2609 2651 76 8726 8802 11 3460 3471 Total 263 3742 4005 104 2425 2529 541 2669 3210 908 8836 9744 99 3478 3577 Accuracy – overall Sensitivity 91.3% PPV % 87.3% 89.4% PPV % 86.1% 92.2% PPV % 89.3% 91.6% PPV 88.3% 88.9% PPV % 83.0% 95%CI (87.2-94.4) (82.7-91.0) (81.9-94.6) (78.1-92.0) (89.7-94.3) (86.4-91.7) (89.6-93.3) (86.1-90.3) (81.0-94.3) (74.5-89.6) Specificity 99.1% NPV % 99.4% 99.4% NPV % 99.5% 97.8% NPV % 98.4% 98.8% NPV 99.1% 99.5% NPV % 99.7% 95%CI (98.7-99.3) (99.1-99.6) (99.0-99.7) (99.2-99.8) (97.1-98.3) (97.9-98.9) (98.5-99.0) (98.9-99.3) (99.2-99.7) (99.4-99.8) Difference in event dates, wks (95% CI) -13.8 (-18.8 to -8.8) -10.1 (-15.3 to -5.1) -20.0 (-23.6 to -16.4) -17.1 (-19.8 to -14.4) -5.5 (-8.9 to -2.2) Accuracy - within 26 weeks Sensitivity 74.9% PPV % 84.9% 78.8% PPV % 84.5% 72.5% PPV % 86.7% 73.9% PPV 85.9% 81.8% PPV % 81.8% 95%CI (69.2-80.0) (79.6-89.3) (69.7-86.2) (75.8-91.1) (68.5-76.2) (83.2-89.7) (70.9-76.7) (83.3-88.3) (72.8-88.9) (72.8-88.9) Specificity 99.1% NPV % 98.3% 99.4% NPV % 99.1% 97.8% NPV % 94.6% 98.8% NPV 97.4% 99.5% NPV % 99.5% 95%CI (98.7-99.3) (97.8-98.6) (99.0-99.7) (98.6-99.4) (97.1-98.3) (93.7-95.4) (98.5-99.0) (97.0-97.7) (99.2-99.7) (99.2-99.7) Local recurrence POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 11 23 34 12 7 19 67 45 112 90 75 165 14 11 25 Event within 12 wks 11 0 11 10 0 10 60 0 60 81 0 81 12 0 12 Event within 12-26 wks 0 0 0 2 0 2 5 0 5 7 0 7 0 0 0 Event > 26 wks 0 0 0 0 0 0 2 0 2 2 0 2 2 0 2 No event 30 3941 3971 15 2495 2510 110 2988 3098 155 9424 9579 16 3536 3552 Total 41 3964 4005 27 2502 2529 177 3033 3210 245 9499 9744 30 3547 3577 Accuracy - overall Sensitivity 26.8% PPV % 32.4% 44.4% PPV % 63.2% 37.9% PPV % 59.8% 36.7% PPV 54.5% 46.7% PPV % 56.0% 95%CI (14.2-42.9) (17.4-50.5) (25.5-64.7) (38.4-83.7) (30.7-45.4) (50.1-69.0) (30.7-43.1) (46.6-62.3) (28.3-65.7) (34.9-75.6) Specificity 99.4% NPV % 99.2% 99.7% NPV % 99.4% 98.5% NPV % 96.4% 99.2% NPV 98.4% 99.7% NPV % 99.5% 95%CI (99.1-99.6) (98.9-99.5) (99.4-99.9) (99.0-99.7) (98.0-98.9) (95.7-97.1) (99.0-99.4) (98.1-98.6) (99.4-99.8) (99.3-99.7) Difference in event dates, wks (95% CI) -5.2 (-7.6 to -2.8) -6.0 (-11.1 to -0.8) -3.0 (-6.0 to -0.0) -3.7 (-6.0 to -1.4) -4.9 (-12.9 to 3.1) Accuracy - within 26 weeks Sensitivity 26.8% PPV % 32.4% 44.4% PPV % 63.2% 36.7% PPV % 59.1% 35.9% PPV 54.0% 40.0% PPV % 52.2% 95%CI (14.2-42.9) (17.4-50.5) (25.5-64.7) (38.4-83.7) (29.6-44.3) (49.3-68.4) (29.9-42.3) (46.0-61.8) (22.7-59.4) (30.6-73.2) Specificity 99.4% NPV % 99.2% 99.7% NPV % 99.4% 98.5% NPV % 96.4% 99.2% NPV 98.4% 99.7% NPV % 99.5% 95%CI (99.1-99.6) (98.9-99.5) (99.4-99.9) (99.0-99.7) (98.0-98.9) (95.7-97.0) (99.0-99.4) (98.1-98.6) (99.4-99.8) (99.2-99.7) Table 2: Level of agreement between NCRAS and trial data for each event type (continued) Breast 2 nd primary POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 24 2 26 14 2 16 42 22 64 80 26 106 15 1 16 Event within 12 wks 24 0 24 14 0 14 39 0 39 77 0 77 15 0 15 Event within 12-26 wks 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Event > 26 wks 0 0 0 0 0 0 3 0 3 3 0 3 0 0 0 No event 12 3967 3979 5 2508 2513 28 3118 3146 45 9593 9638 7 3554 3561 Total 36 3969 4005 19 2510 2529 70 3140 3210 125 9619 9744 22 3555 3577 Accuracy – overall Sensitivity 66.7% PPV % 92.3% 73.7% PPV % 87.5% 60.0% PPV % 65.6% 64.0% PPV 75.5% 68.2% PPV % 93.8% 95%CI (49.0-81.4) (74.9-99.1) (48.8-90.9) (61.7-98.4) (47.6-71.5) (52.7-77.1) (54.9-72.4) (66.2-83.3) (45.1-86.1) (69.8-99.8) Specificity 99.9% NPV % 99.7% 99.9% NPV % 99.8% 99.3% NPV % 99.1% 99.7% NPV 99.5% 100.0% NPV % 99.8% 95%CI (99.8-100.0) (99.5-99.8) (99.7-100) (99.5-99.9) (98.9-99.6) (98.7-99.4) (99.6-99.8) (99.4-99.7) (99.8-100.0) (99.6-99.9) Difference in event dates, wks (95% CI) -0.2 (-0.8 to 0.7) 2.1 (0.7-3.6) 9.3 (0.2 to 18.5) 5.2 (0.4 to 10.1) 2.3 (0.6 to 4.0) Accuracy - within 26 weeks Sensitivity 66.7% PPV % 92.3% 73.7% PPV % 87.5% 55.7% PPV % 63.9% 61.6% PPV 74.8% 68.2% PPV % 93.8% 95%CI (49.0-81.4) (74.9-99.1) (48.8-90.9) (61.7-98.4) (43.3-67.6) (50.6-75.8) (52.5-70.2) (65.2-82.8) (45.1-86.1) (69.8-99.8) Specificity 99.9% NPV % 99.7% 99.9% NPV % 99.8% 99.3% NPV % 99.0% 99.7% NPV 99.5% 100.0% NPV % 99.8% 95%CI (99.8-100.0) (99.5-99.8) (99.7-100) (99.5-99.9) (98.9-99.6) (98.6-99.3) (99.6-99.8) (99.3-99.6) (99.8-100.0) (99.6-99.9) Non-breast 2 nd primary POETIC IMPORT HIGH TACT2 Combined training trials FAST-Forward Number of Events Event No Event Total Event No Event Total Event No Event Total Event No Event Total Event No Event Total Total Events 132 44 176 21 7 28 105 50 155 258 101 359 35 22 57 Event within 12 wks 123 0 123 18 0 18 95 0 95 236 0 236 32 0 32 Event within 12-26 wks 4 0 4 1 0 1 7 0 7 12 0 12 1 0 1 Event > 26 wks 5 0 5 2 0 2 3 0 3 10 0 10 2 0 2 No event 19 3807 3825 3 2498 2501 5 3050 3055 27 9355 9381 13 3507 3520 Total 151 3851 4005 24 2505 2529 110 3100 3210 285 9456 9744 48 3529 3577 Accuracy - overall Sensitivity 87.4% PPV % 75.0% 87.5% PPV % 75.0% 95.5% PPV % 67.7% 90.5% PPV 71.9% 72.9% PPV % 61.4% 95%CI (81.0-92.3) (67.9-81.2) (67.6-97.3) (55.1-89.3) (89.7-98.5) (59.8-75.0) (86.5-93.7) (66.9-76.5) (58.2-84.7) (47.6-74.0) Specificity 98.9% NPV % 99.5% 99.7% NPV % 99.9% 98.4% NPV % 99.8% 98.9% NPV 99.7% 99.4% NPV % 99.6% 95%CI (98.5-99.2) (99.2-99.7) (99.4-99.9) (99.6-100) (97.9-98.8) (99.6-99.9) (98.7-99.1) (99.6-99.8) (99.1-99.6) (99.4-99.8) Difference in event dates, wks (95% CI) 1.2 (-0.5 to 3.0) -6.8 (-24.4 to 10.8) 2.0 (0.2 to 3.7) 0.9 (-0.9 to 2.6) 6.3 (-0.9 to 13.6) Accuracy - within 26 weeks Sensitivity 84.1% PPV % 74.3% 79.2% PPV % 73.1% 92.7% PPV % 67.1% 87.0% PPV 71.1% 68.8% PPV % 60.0% 95%CI (77.3-89.5) (67.0-80.6) (57.8-92.9) (52.2-88.4) (86.2-96.8) (59.0-74.5) (82.6-90.7) (66.0-75.8) (53.7-81.3) (45.9-73.0) Specificity 98.9% NPV % 99.4% 99.7% NPV % 99.8% 98.4% NPV % 99.7% 98.9% NPV 99.6% 99.4% NPV % 99.6% 95%CI (98.5-99.2) (99.1-99.6) (99.4-99.9) (99.5-99.9) (97.9-98.8) (99.5-99.9) (98.7-99.1) (99.5-99.7) (99.1-99.6) (99.3-99.8) In the example of TTR, further detail regarding mis-classifications for each individual event/non-event by trial is shown in Figures A2A-H. Considering distant recurrence in particular, where distant recurrence was reported in the trial data but not identified by the procedure in the NCRAS dataset (n = 87); 6 (6.9%) patients with distant recurrence events across the four trials had a second primary cancer identified within NCRAS. Similarly, where the procedure identified a distant recurrence within NCRAS datasets that did not exist within the trial datasets (n = 128), 26 (20.3%) had a second primary cancer recorded within the trial data. Almost always events were identified earlier in the trial data than in NCRAS due to identification of an event within NCRAS needing to be substantiated by collective activity rather than sporadic episodes of activity that might indicate a suspicion of recurrence. TTR events were, on average, identified 15.1 weeks earlier (95%CI:-18.0to-12.3) in the trial data than from NCRAS data (Table 1 ). Similarly, for iDFS the difference was 8.6 weeks earlier (95%CI: -10.6to-6.5) in the trial data than from NCRAS data. Similar differences between the trial data and NCRAS data were found for individual events (Table 2 ). As expected, there was no evidence of a difference in identifying death events between the trial data and NCRAS data (Table 1 ). Despite the challenges with matching, and the observed time delay in identifying events within NCRAS datasets, the survival estimates for the survival-related endpoints (TTR, iDFS and OS) for the individual RCTs were similar between NCRAS and trial data (Fig. 2 ). For example, 3-year estimates for TTR for POETIC were 94.5% (95%CI:93.8–95.2) using NCRAS and 95.2% (95%CI:94.5–95.8) within the trial. Similarly, HR for treatment effects were comparable between NCRAS and trial data indicating that any time differences for event detection were systematically applied across treatment groups (Tables A1 and A2). For example, HR for TTR in POETIC were 1.08 (95%CI:0.86–1.37); p = 0.51 for NCRAS and 1.10 (95%CI:0.86–1.41); p = 0.45 for trial data. Discussion This work has shown that it is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis. Good levels of agreement were found for the composite endpoints tested and their individual component event-types. In addition, survival analyses showed that if NCRAS data were used in place of hospital-based follow-up, treatment effects would have been similar and conclusions drawn would have been the same for all four trials. Little work has been done to date around accuracy of disease-related endpoints such as TTR and iDFS within breast cancer and most of the data is not collected within the realms of a clinical trial and/or outside of the UK. An abstract by Mannu et al. in 2016 reported data from over 53,000 women in the West Midlands, UK, comparing recurrence information recorded in the National Cancer Registration Service in Birmingham to national RCHD datasets showed high agreement between these datasets with 92% of the patients having recurrence in either breast or lymph nodes, distant metastases or a second primary cancer identified within RCHD( 14 ); however this is comparing one local cancer registry who have a particular interest in breast cancer with national datasets and so is not directly comparable to our work comparing RCHD with data collected as part of a RCT. Outside of the UK, a Danish study by Rasmussen et al. in 2019( 15 ), showed almost perfect agreement between Danish health registers and Danish Breast Cancer Group data for 471 women with early stage breast cancer, with a sensitivity of 97.3% (95%CI:93.2–99.3), specificity of 97.2% (95%CI:94.8–98.7), indicating that it is possible to achieve high accuracy in RCHD in well-curated datasets. In the US, Chubak et al.( 16 ) reported good sensitivity of 89% (95%CI:84–92), specificity of 99% (95%CI:98–99) and PPV of 90% PPV (95%CI:86–94) comparing recurrences identified in SEER cancer registry data with medical record review in 3152 women with early stage breast cancer. In Canada, Jung et al.( 17 ) reported moderate-good agreement between RCHD and chart-reviewed breast cancer recurrence data for 598 patients with sensitivity of 94.2% and PPV of 79.2%. Notably, approximately two-thirds of their algorithm-estimated recurrence dates fell within 3 months of the chart-reviewed dates which is similar to the mean difference in times that we saw for this project. Elsewhere, in other cancer sites, some data are available comparing RCHD to either trial data or local source data with mixed results. A study by Mintz et al. using data from the STAMPEDE trial in patients with prostate cancer showed that a composite endpoint including elements from failure-free survival (FFS), metastases free survival (MFS) and PFS, developed using HES data, was comparable to the reported MFS endpoint in STAMPEDE – HES composite endpoint HR = 0.88 (95%CI: 0.77–1.01) versus STAMPEDE MFS HR = 0.82 (95%CI: 0.71–0.95)( 18 ). In addition, a study by Kelly et al. in 2017 showed acceptable agreement of 35/50 patients identified within routine data for PFS in Glioblastoma, however PFS estimation was less accurate than OS( 19 ). In head and neck cancer, Ricketts et al.( 20 ) calculated OS and PFS from routinely collected local data on inpatient admission and procedures, chemotherapy and radiotherapy and compared it with data collected directly from hospital notes for 122 patients. It showed good agreement for their automatic technique using RCHD versus hospital note data of 98% and 82% for OS and PFS respectively; although they noted that it underestimated recurrence rates due to lack of patients being treated for recurrent disease in this setting. Within our project, recurrences were most challenging to identify in approximately the first 15 months after diagnosis due to the overlap with the intensive treatment period for primary disease where treatment such as chemotherapy, anti-HER2 therapy and re-excisions of primary surgery would occur. This is a particular challenge in breast cancer subtypes at higher risk of early recurrence, such as triple negative breast cancer. Additional challenges with identifying local recurrences in particular are, if occurring in isolation, localised treatment (e.g. further surgery, radiotherapy) is not always well-recorded; or, if occurring around the time of a subsequent distant recurrence diagnosis, HES data will be dominated by systemic treatment for the metastases thus making the local recurrence hard to spot. Using RCHD in the long-term follow-up setting would avoid the overlap with the initial cancer treatment and events would be dominated by distant recurrence and, with increase age of trial participants, second primary cancers. Misclassification of distant recurrences with second primary cancers, in addition, is a challenge when comparing trial data collected in “real-time” to routine data sourced later. The appearance of new disease, thought to be distant recurrence at the time and treated as such, may later be identified as a second primary cancer, e.g. via a post-mortem. It is also known that as patients get older, the incidence of second primary cancers increases; therefore, endpoints such as iDFS are robust to misclassification and arguably more relevant than TTR in the long-term follow-up setting when late effects of primary treatment, such as development of second primary cancers, need to be considered. However, with improving diagnosis methods and the increasing importance of biopsying metastatic disease to inform personalised treatment decisions, correct classification is likely to improve in future. More detailed coding, generally, within routine datasets would assist in identifying recurrences more easily. For example, when developing the procedure, it was found that the HES data that details the purpose of a visit to medical oncology was regularly incomplete therefore it was not truly possible to know if the patient was receiving cancer treatment. In addition to improved coding for the hospital episode itself, the use of ICD10 ‘C’ codes would be beneficial alongside the treatment details provided within the medical oncology visit to help substantiate a cancer event. The majority of the recurrence events have been identified from key episodes in HES data but the definitive diagnosis date of the recurrence is not recorded so the episode date is used as a surrogate. In some cases this surrogate date may not be very accurate, for example, where a patient has received a treatment that may not be well documented in HES such as hormone therapy which does not require hospital admission. Our analysis showed that routine data frequently identified events later down the patient’s pathway; however, this difference did not impact significantly on overall analysis of survival outcomes. Whilst we believe our procedure for identifying recurrences works, improvements can always be made. Upon manual inspection of the patients with no distant recurrence event identified within RCHD, 40/87 (46%) had treatment data within SACT and/or RTDS datasets that could have indicated a distant recurrence event; however, automating the use of this data field within these two datasets would have potentially increased the number of false positives identified and therefore needs to be balanced against this risk. Further work is planned to reduce the diagnosis date discrepancies identified between RCHD and trial data by making smarter use of SACT and RTDS data available to pick up additional treatment information and dates not recorded within HES and to add certainty/weight to the events found. In addition, consideration has been made to applying machine learning/AI methods to the datasets to maximise use of all available data, identifying the key fields for recurrence identification and improving the automation of this process; acknowledging the limitations of the currently available AI models. A further limitation of our work is that the RCHD datasets used within this project are now quite old, although the majority of fields have remained the same. We had little data available from the COSD dataset at the time as it was relatively new and incomplete, similarly the SACT data was incomplete and a challenge to process. COSD specifically requests data on recurrence, updated data fields in 2019 include type of recurrence, so this dataset has the potential to replace existing identification practices if it has good completeness and data quality and will help ameliorate the challenge of identifying recurrence events close to initial diagnosis. This will need to be confirmed once the COSD dataset matures. Challenges with accessing RCHD in the UK, in terms of time, cost and data governance requirements over and above that already needed to run RCTs within the existing regulatory framework, in addition to the remaining data quality issues, are well-documented( 21 , 22 ). In summary, using RCHD for long term follow-up as an alternative data source to hospital-based data collection is possible and with continual improvements in data quality, completeness and detail, it would indicate that current RCHD datasets would provide a suitable alternative to hospital-based data collection for long term follow-up of trial participants. The research community owe it to trial participants to ensure that the mechanisms to obtain such data are timely, feasible and affordable. Declarations Ethics approval and consent to participate For all four trials used within this research project patients had provided informed consent for access to routine medical records (POETIC: London–South East Research Ethics Committee (08/H1102/37); TACT2: Scotland Multi-Research Ethics Committee (04/MRE00/88); IMPORT HIGH: Cambridgeshire 4 Research Ethics Committee (08/H0305/13); FAST-Forward: South East Coast Kent Research Ethics Committee (11/LO/0958)), therefore no additional approval was required to receive data from NHS England. Consent for publication Not applicable Availability of data and materials Trial data are available from the authors upon reasonable request as per ICR-CTSU data and sample access policy. See website for further details: https://www.icr.ac.uk/our-research/centres-and-collaborations/centres-at-the-icr/clinical-trials-and-statistics-unit/working-with-us/data-sharing. Datasets provided by NHS England were used with specific approval for this project thus restrictions apply to the availability of this data. Competing interests The authors declare that they have no competing interests Funding RCHD datasets for each trial were kindly provided by NHS England (formally Public Health England) free of charge for use within this research project. The ICR-CTSU receives core programme grant funding from Cancer Research UK (grant numbers C1491/A25351 and C1491/A15955) which supported this research project. This paper represents independent research part funded by the NIHR Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and the Institute of Cancer Research. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Authors' contributions LSK initiated, designed the project, analysed and interpreted the data and co-wrote the manuscript. VH and SGM analysed and interpreted the data and co-wrote the manuscript. JMB initiated, designed the project, analysed and interpreted the data and co-wrote the manuscript. Acknowledgements We thank our patients, the investigators and the research support staff at all participating centres, the trial management groups and trial steering committees for the four trials used for this research project. We also thank staff at NHS England as data for this project includes information collected and quality assured by the NHS England National Cancer Registration and Analysis Service. Access to the data was facilitated by the PHE Office for Data Release, now part of NHS England. References https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer/survival; Accessed 17 July 2024. 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Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: An overview of the randomised trials. Lancet. 2005 Dec 17;366(9503):2087–106 Mintz HP, Dosanjh A, Parsons HM, Hughes A, Jakeman A, Pope AM, Bryan RT; BladderPath trial management group, James ND, Patel P. Development and validation of a follow-up methodology for a randomised controlled trial, utilising routine clinical data as an alternative to traditional designs: a pilot study to assess the feasibility of use for the BladderPath trial. Pilot Feasibility Stud. 2020 Oct 31;6(1):165. Murray ML, Love SB, Carpenter JR, Hartley S, Landray MJ, Mafham M, Parmar MKB, Pinches H, Sydes MR; Healthcare Systems Data for Clinical Trials Collaborative Group. Data provenance and integrity of health-care systems data for clinical trials. Lancet Digit Health. 2022 Aug;4(8):e567-e568. 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A historical baseline on which to build: retrospective linkage of data from the TACT (CRUK 01/001) breast cancer trial and the National Cancer Data Repository. Trials. 2017 Nov 23;18(1):561. Smith I, Robertson J, Kilburn L, Wilcox M, Evans A, Holcombe C, Horgan K, Kirwan C, Mallon E, Sibbering M, Skene A, Vidya R, Cheang M, Banerji J, Morden J, Sidhu K, Dodson A, Bliss JM, Dowsett M. Long-term outcome and prognostic value of Ki67 after perioperative endocrine therapy in postmenopausal women with hormone-sensitive early breast cancer (POETIC): an open-label, multicentre, parallel-group, randomised, phase 3 trial. Lancet Oncol. 2020 Nov;21(11):1443-1454. Murray Brunt A, Haviland JS, Wheatley DA, Sydenham MA, Alhasso A, Bloomfield DJ, Chan C, Churn M, Cleator S, Coles CE, Goodman A, Harnett A, Hopwood P, Kirby AM, Kirwan CC, Morris C, Nabi Z, Sawyer E, Somaiah N, Stones L, Syndikus I, Bliss JM, Yarnold JR; FAST-Forward Trial Management Group. Hypofractionated breast radiotherapy for 1 week versus 3 weeks (FAST-Forward): 5-year efficacy and late normal tissue effects results from a multicentre, non-inferiority, randomised, phase 3 trial. Lancet. 2020 May 23;395(10237):1613-1626. Coles CE, Haviland JS, Kirby AM, Griffin CL, Sydenham MA, Titley JC, Bhattacharya I, Brunt AM, Chan HYC, Donovan EM, Eaton DJ, Emson M, Hopwood P, Jefford ML, Lightowlers SV, Sawyer EJ, Syndikus I, Tsang YM, Twyman NI, Yarnold JR, Bliss JM; IMPORT Trial Management Group. Dose-escalated simultaneous integrated boost radiotherapy in early breast cancer (IMPORT HIGH): a multicentre, phase 3, non-inferiority, open-label, randomised controlled trial. Lancet. 2023 Jun 24;401(10394):2124-2137. Cameron D, Morden JP, Canney P, Velikova G, Coleman R, Bartlett J, Agrawal R, Banerji J, Bertelli G, Bloomfield D, Brunt AM, Earl H, Ellis P, Gaunt C, Gillman A, Hearfield N, Laing R, Murray N, Couper N, Stein RC, Verrill M, Wardley A, Barrett-Lee P, Bliss JM; TACT2 Investigators. Accelerated versus standard epirubicin followed by cyclophosphamide, methotrexate, and fluorouracil or capecitabine as adjuvant therapy for breast cancer in the randomised UK TACT2 trial (CRUK/05/19): a multicentre, phase 3, open-label, randomised, controlled trial. Lancet Oncol. 2017 Jul;18(7):929-945. Mannu GS, Broggio J, Charman J, Darby S. Identifying recurrence in breast cancer patients from routinely collected data in England. Eur J Surg Oncol. 2016; 42(5): PS33-S34. Rasmussen LA, Jensen H, Virgilsen LF, Thorsen LBJ, Offersen BV, Vedsted P. A validated algorithm for register-based identification of patients with recurrence of breast cancer—Based on Danish Breast Cancer Group (DBCG) data. Cancer Epidem.,2019; 59: 129-134. Chubak J, Yu O, Pocobelli G, Lamerato L, Webster J, Prout MN, Ulcickas Yood M, Barlow WE, Buist DS. Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer. J Natl Cancer Inst. 2012; Jun 20;104(12):931-40. Jung H, Lu M, Quan ML, Cheung WY, Kong S, Lupichuk S, Feng Y, Xu Y. New method for determining breast cancer recurrence-free survival using routinely collected real-world health data. BMC Cancer 2022; 22: 281. Mintz HP. Can routinely collected data be used to inform randomised controlled trial outcomes in oncology. University of Warwick, 2019. Kelly C, Majewska P, Ioannidis S, Raza MH, Williams M. Estimating progression-free survival in patients with glioblastoma using routinely collected data. J Neurooncol. 2017 Dec;135(3):621-627. Ricketts K, Williams M, Liu ZW, Gibson A. Automated estimation of disease recurrence in head and neck cancer using routine healthcare data. Comput Methods Programs Biomed. 2014 Dec;117(3):412-24. Macnair A, Love SB, Murray ML, Gilbert DC, Parmar MKB, Denwood T, Carpenter J, Sydes MR, Langley RE, Cafferty FH. Accessing routinely collected health data to improve clinical trials: recent experience of access. Trials 22, 340 (2021). Appleyard SE, Gilbert DC. Innovative Solutions for Clinical Trial Follow-up: Adding Value from Nationally Held UK Data. Clinical Oncol. 2017; 29(12): 789-795. Supplementary Files DataLinkageSupplementaryMaterial20240717.pdf Cite Share Download PDF Status: Published Journal Publication published 26 Sep, 2025 Read the published version in Trials → Version 1 posted Editorial decision: Major revision 01 Jun, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 31 Jul, 2024 Editor assigned by journal 31 Jul, 2024 First submitted to journal 22 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4780757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334236786,"identity":"aa01d318-2c21-4514-80ed-57e1adea93f9","order_by":0,"name":"Lucy Suzanne Kilburn","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie2RMQrCMBSGn1TSJdj1ieIZHgi2BelZCoG6VPQE4gE8gIOHqQhOPUDGFiFTh44ODiZFBQeDbg75hgQe+Xj/TwAcjj9k0J0FjsncLT3nxWeFPRRuHvf2PyjQKR5/zW0KCoVQRjzEpbrM18kK/FPl8dKmZDMEiTzeL8JpTiLe8ow8Lm1KzhBa3UVmbJRToQPmOmH7rRIZJWi+UeRDAaOg2WILxtU0SkvdZafYcEeCGCo6Hiz1A1/Usj1vJqGfMbzeEgoCUVfN+bOi6WP6thesv9LhWao6HA6HQ3MHhJ9AwY9/xy0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1987-7545","institution":"Institute of Cancer Research","correspondingAuthor":true,"prefix":"","firstName":"Lucy","middleName":"Suzanne","lastName":"Kilburn","suffix":""},{"id":334236787,"identity":"cbeeb276-d97c-45da-8230-acf1e98229ab","order_by":1,"name":"Victoria Hinder","email":"","orcid":"","institution":"The Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Victoria","middleName":"","lastName":"Hinder","suffix":""},{"id":334236788,"identity":"b5de68e8-c3fd-4d44-b454-dc2f976f8f45","order_by":2,"name":"Sikhuphukile Gillian Ndebele-Mahati","email":"","orcid":"","institution":"The Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Sikhuphukile","middleName":"Gillian","lastName":"Ndebele-Mahati","suffix":""},{"id":334236789,"identity":"1f261546-cb65-4ee9-b521-f5ccd98d83a2","order_by":3,"name":"Judith M Bliss","email":"","orcid":"","institution":"The Institute of Cancer Research","correspondingAuthor":false,"prefix":"","firstName":"Judith","middleName":"M","lastName":"Bliss","suffix":""}],"badges":[],"createdAt":"2024-07-22 09:37:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4780757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4780757/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13063-025-09085-1","type":"published","date":"2025-09-26T15:58:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64142648,"identity":"0140be28-b3a9-4019-be8a-7cf71e026667","added_by":"auto","created_at":"2024-09-08 19:01:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":726868,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of procedure for disease-related event identification\u003c/p\u003e","description":"","filename":"Figure120240718FINAL.png","url":"https://assets-eu.researchsquare.com/files/rs-4780757/v1/d36bb2fa59679baeacc664df.png"},{"id":64142649,"identity":"a1e9b0b3-8221-44dc-8e8f-9034051d7deb","added_by":"auto","created_at":"2024-09-08 19:01:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":207580,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan Meier curves and survival estimates for each disease-related outcome by trial\u003c/p\u003e","description":"","filename":"Figure217Jul2024.png","url":"https://assets-eu.researchsquare.com/files/rs-4780757/v1/4e1bd520467b9c59f38b1909.png"},{"id":92430773,"identity":"9e53bac7-58b6-4c03-a046-af8688b6b56b","added_by":"auto","created_at":"2025-09-29 16:07:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4391099,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4780757/v1/e6564ed3-b01e-40f1-8304-b3032c3aaa46.pdf"},{"id":64142650,"identity":"6fa940fd-be19-42cc-916e-b5e807e9aa4f","added_by":"auto","created_at":"2024-09-08 19:01:43","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":1023397,"visible":true,"origin":"","legend":"","description":"","filename":"DataLinkageSupplementaryMaterial20240717.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4780757/v1/0c50147c628b4a5b9bc176af.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eUse of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: A practical alternative to hospital-based follow-up for breast cancer clinical trials\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common cancer affecting women in the UK with over 55,000 cases per year. Incidence data between 2013\u0026ndash;2017 show that over 85% of women survive for five years or more and 76% survive for 10 years or more, with survival rates doubling over the last 40 years(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This is encouraging news for patients, but it also means that participants in breast cancer clinical trials need to be followed-up for longer than the typical 3 or 5-year analysis timepoint to fully establish the long-term impact of treatments under investigation. For example, recurrence risk for hormone sensitive breast cancer extends beyond 20 years with the impact of benefits and risks of adjuvant treatment detectable throughout that follow-up period(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRandomised controlled trials (RCTs) are recognised to be the optimal scientifically rigorous method for hypothesis testing and considered the gold standard approach to evaluate the effectiveness of a treatment intervention. However, long-term assessment of both disease-related outcomes and treatment-related sequelae can be challenging for trialists given the effort required for data collection and comes at a considerable cost to research funders and participating NHS provider sites; a cost many funders are often no longer willing to bare. Coupled with competing pressures within the clinical setting, where oncology patients are routinely discharged early from hospital-based follow-up and NIHR CRN is resource constrained, there is an increased threat to researchers\u0026rsquo; ability to collect the requisite follow-up information. In an era which foresees greater integration of data, there is an increasing resolve within the clinical trials community to minimise data collected via completion of case report forms (CRFs) and to advocate the use of routinely collected data sources where possible. This will be of considerable importance going forward, given the broad recognition that clinically important consequences following breast cancer treatment, such as impact on cardiovascular disease, may only become detectable\u0026thinsp;\u0026gt;\u0026thinsp;10 years after treatment delivery(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The immediate challenge in the UK however is that in spite of inclusion in the Cancer Outcomes and Services Dataset (COSD) cancer recurrences, per se, are not universally collected in a single identifiable data field as is established for cancer diagnoses. This leaves a need for derivation of procedures capable of interrogating multiple datasets and extracting patterns of activity in health data which predict for a cancer recurrence. Clinical trials which have mature follow-up for disease-related outcomes provide excellent vehicles for validation of such procedures including estimation of their positive and negative predictive value.\u003c/p\u003e \u003cp\u003eUK routinely collected healthcare data (RCHD) has already been shown to be a reliable source of information in terms of treatment delivery and overall survival (OS) in bladder cancer(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and, specifically, an evaluation of data provenance and integrity of hospital episode statistics and death outcomes has shown that these datasets have robust curation equivalent to high quality source data, therefore can be considered sufficiently reliable for use in clinical trials(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, uptake of use of RCHD within clinical trials remains low with an estimated 3% of data releases from UK registries between 2013\u0026ndash;2018 being linked to clinical trials, despite it seeming to be a sensible alternative for data collection given the burden hospital-based CRF completion places on the NHS(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the challenges with using RCHD in cancer clinical trials however and a major reason for its low usage to date, is the lack of systematically collected information relating to recurrence following a cancer diagnosis. Disease-related endpoints used within breast cancer trials, such as invasive disease-free survival (iDFS; 8), are composite endpoints formed of multiple types of disease-related event, namely for iDFS; local recurrence, distant recurrence, new second primary cancer and death. Primary breast cancer diagnosis and any subsequent second primaries are recorded as new diagnoses within cancer registry datasets; however, local or distant recurrences linked to the primary breast cancer are not reliably or consistently collected within currently available RCHD. Working on the premise that upon diagnosis of recurrence a large proportion of breast cancer patients will go on to receive further treatment as part of standard-of-care, we aimed to identify a procedure to identify recurrences within RCHD. We wanted to explore whether key episodes or events within the RCHD datasets following a patient\u0026rsquo;s initial breast cancer diagnosis could be used as reliable indicators that the cancer had returned. In order to establish validity of any such derivation comparison against a reliable source is required. We postulate that data collected prospectively for the purposes of large, multi-centre academically sponsored RCTs with mature clinical data on disease-related outcomes form such a gold-standard and have thus linked data from four UK academically sponsored breast cancer trials. This work builds upon a previous project, comparing an older subset of linked cancer registration data (held in the National Cancer Data Repository (NCDR)) and data from the TACT breast cancer trial, that established a high degree of matching between NCDR and trial data(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eFour early breast cancer trials were chosen for this project to provide a combined \u0026ldquo;gold standard\u0026rdquo; cohort against which an RCHD derivation of a subsequent breast cancer disease related event could be compared. These were (POETIC (ISRCTN63882543), IMPORT HIGH (ISRCTN18654225), TACT2 (ISRCTN68068041) and FAST-Forward (ISRCTN19906132)) managed by The Institute of Cancer Research Clinical Trials \u0026amp; Statistics Unit (ICR-CTSU) with English RCHD managed by the National Cancer Registry and Analysis Service (NCRAS), formally within Public Health England (PHE), now part of NHS England. Trials had mature follow-up (median 60.9, 42.0, 108.4 and 41.2 months for the 4 trials above respectively) and collectively included over 13,324 patients who had been treated at hospitals across England. Relevant participants from the four trials were identified by ICR-CTSU in accordance with the following inclusion criteria: that the patient had histologically confirmed breast cancer, the patient had valid consent to link to medical records/access medical records and the patient had a valid NHS number within England and Wales to allow for transfers between countries (i.e. excluding patients with CHI numbers). NCRAS performed deterministic matching to the following datasets: Cancer Registry (including vital status) and Cancer Outcomes and Services Dataset (COSD), Hospital Episode Statistics \u0026ndash; admitted patient care (HES APC), \u0026ndash; outpatients (HES OP) and \u0026ndash; accident and emergency (HES A\u0026amp;E), Radiotherapy Dataset (RTDS) and Systemic Anti-Cancer Therapy Dataset (SACT). Patients were considered correctly matched if their date of birth was the same in the Cancer Registry and trial datasets.\u003c/p\u003e \u003cp\u003eDevelopment of a disease-related event identification procedure within RCHD was via identification of key events within a patient\u0026rsquo;s pathway that were considered highly-likely to be indicative of a recurrence. Events identified via this procedure would then be used as a proxy for confirmation of recurrence minimising use of known patient specific information (e.g. avoiding use of exact treatment details) to make it as generalizable as possible whilst maximising accuracy. The first aim was to identify the initial breast cancer diagnosis, second primary cancers and deaths given their anticipated ease of ascertainment as RCHD registry landmark events. How recurrence would be identified was less obvious, but the initial plan was to look for activity related to the management of the cancer. E.g. biopsies, scans, delivery of cancer treatment. Once each individual event was identified they would be compared to the trial data and adjustments made to the process.\u003c/p\u003e \u003cp\u003eThe procedure was developed (trained) on POETIC trial data initially, then tested and refined using TACT2 and IMPORT HIGH data. In an iterative approach, the development of the procedure involved initial identification of disease-related events conducted blind to the trial outcome data and then the procedure was refined if improvements could be made after initial matching. FAST-Forward data was used as a separate internal validation of the full working disease-related event identification procedure, thus all recurrence, second primary and death events were identified first within the RCHD blinded to trial outcome data then compared to the trial outcome data as the final step. At this point mis-classifications between the RCHD and trial data were assessed, for example, trial data recording an event as a distant recurrence but recorded as a second primary within RCHD. In addition, consideration was given to whether any of the components could be improved by knowledge of another, e.g. information on the death indicates metastatic relapse.\u003c/p\u003e \u003cp\u003eTo ensure a fair comparison to assess how well the routine data identification matched to the trial data, patients were censored at the date of their last follow-up available from the HES datasets or the patient\u0026rsquo;s last trial follow-up, whichever was the earliest. However, when the endpoint was OS the last follow-up date available from the Cancer Registry dataset was used as death data is updated within NCRAS more frequently. Time in follow-up between RCHD datasets and trial data was explored using reverse Kaplan Meier and the mean difference and 95% CI were reported. For the purpose of classification of a match between clinical trial and RCHD, all event dates (recurrence, second primary cancer or death) within 12 weeks of each other were classed as agreement.\u003c/p\u003e \u003cp\u003eWhere the number of events allowed for each event-type, measures of agreement including sensitivity and specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated to assess matching between routine data and trial data for linked patients for each of the four trials separately and for the three test trials combined. Scatter plots were used to visualise closeness of matches and possible reasons for an event but no match; for example, different classifications of an event or where an event was close to the last known follow-up date for a given dataset. In addition, estimations of the differences between dates when events were identified in the NCRAS datasets versus the trial data were reported via mean and 95%CIs. In addition, once the disease-related event identification procedure was completed for each trial, survival-related outcomes relevant to long term follow-up of time to recurrence (TTR equivalent to Relapse Free Interval (RFI) in STEEP2(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)- defined as time from randomisation to local, regional, or distant tumour recurrence or death from breast cancer without prior notification of relapse. Second primary cancers and intercurrent deaths were treated as censoring events. Patients who were alive and disease free were censored at the date last seen alive), iDFS (defined as time from randomisation until first confirmed relapse of this breast cancer, new second primary or death from any cause)(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and OS were calculated using the RCHD and separately using clinical trial data for eligible, linked patients only. Survival-related endpoints were presented using Kaplan Meier plots. Hazard ratios (HR) using Cox regression modelling, log rank test p-values and three-year survival-rates for the randomised treatments were reported separately for RCHD and trial data. For TACT2, the trial with the longest follow up, seven-year estimates were also reported to show estimates in the long-term follow-up setting.\u003c/p\u003e \u003cp\u003eLevels of agreement and estimates of treatment effect were used to assess whether routinely RCHD can be used as an alternative to hospital-based follow-up in patients participating in breast cancer trials.\u003c/p\u003e \u003cp\u003eData processing and analyses were conducted using Stata version 16.1 and latterly, version 17.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eNCRAS linked RCHD datasets for the four clinical trials were received by ICR-CTSU between January-April 2018. These included tumour data up to 31/12/2015, HES data to 31/01/2016 and death data to 31/12/2016. SACT data and RTDS data were reported up to 28/02/2017 and 31/03/2016 respectively. For 3 of the 4 trials (POETIC, FAST-Forward and IMPORT HIGH) data snapshots where those used previously for the primary publication of the clinical trial results(\u003cspan\u003e10\u003c/span\u003e\u0026ndash;\u003cspan\u003e12\u003c/span\u003e). For the fourth (TACT2), where publication of results been based on an earlier data snapshot(\u003cspan\u003e13\u003c/span\u003e), the snapshot taken for the primary analysis was initially used but later surpassed with an updated, unpublished dataset to include the additional time period of data received by NCRAS. The majority of NHS numbers that were sent to NCRAS were successfully linked (overall 93%). Data from patients resident in Wales who had some treatment at English hospitals post-diagnosis were not included in the analysis due to sporadic availability of NCRAS data. Overall, based on the trial datasets 524 (3.9%) patients were classed as lost to follow-up within the four trials based on information from hospital staff (Figure A1).\u003c/p\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003eProcedure development\u003c/h2\u003e\n \u003cp\u003eInitial breast cancer diagnoses were identified via a new cancer diagnosis in the Cancer Registry dataset and the distribution of the dates compared to the trial data were reviewed to establish whether they should be considered related to the initial diagnosis. Date of surgery for removal of primary disease and date of randomisation were used as surrogates for date of diagnosis in the trial datasets. After reviewing the data, a window of up to 12 weeks post-randomisation or surgery for removal of primary in the absence of diagnosis date within the trial data was deemed acceptable to classify as due to the initial diagnosis. Fields used to identify breast and non-breast cancer diagnoses are included in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eIdentifying date of death was straightforward within routine datasets via a single field within the Cancer Registry dataset. Identifying whether the cause of death was breast cancer related, in particular whether the patient had metastatic disease or not, was established using the cause of death fields available (see supplementary material for further details). Second primary cancers were identified from the new cancer diagnosis field in the Cancer Registry dataset. Only invasive cancers were relevant in this setting for the trials selected and invasive status was determined by morphology and behaviour ICD codes and sites of disease (e.g. skin basal cell carcinomas were counted as non-invasive and therefore not counted as an event)\u003c/p\u003e\n \u003cp\u003eDisease recurrence was identified via looking for cancer treatment activities in the cancer registry treatment dataset, HES OP, HES APC, RTDS and SACT datasets. Exploration of this involved two methods to identify metastatic disease that were later rejected (see supplementary materials for details) before identifying recurrence by using the first occurrence of the ICD-10 diagnosis codes C77X (excluding C773 codes as these represent cancer in axillary nodes), C78X and C79X within the HES OP and HES APC diagnosis fields that are provided at each hospital visit. These ICD-10 codes do not indicate the primary cancer diagnosis, it was therefore assumed that the metastatic disease was for breast cancer unless the patient had a non-breast second primary cancer diagnosed before or within 8 weeks of the metastatic diagnosis. In this case the metastatic disease was attributed to the second primary cancer. The RTDS has a field that indicates the intent (curative, palliative) of the radiotherapy; therefore, this was used to provide an additional check on a patient\u0026rsquo;s metastatic disease status. In addition, the SACT dataset was reviewed for the type of treatment and assigned into the categories \u0026ldquo;adjuvant\u0026rdquo;, \u0026ldquo;metastatic\u0026rdquo;; providing an additional check on a patient\u0026rsquo;s metastatic status. Additional RTDS and SACT data fields such as specific treatment details were reviewed and included as part of the initial procedure development, but for simplicity, with the risk of introducing a higher rate of false positives, and desire for a more generalisable, future-proof procedure they were not formally included in the final procedure. In addition, where cause of death included metastatic breast cancer this was used to identify metastatic disease if not picked up in the HES data.\u003c/p\u003e\n \u003cp\u003eDetermining local recurrence was the most problematic as it was not easy to distinguish events due to the patient\u0026rsquo;s initial disease and subsequent recurrence. The ICD-10 code C50X represents a breast cancer diagnosis, however it may be used also when a patient is having ongoing treatment for cancer regardless as to whether the cancer is still present. Therefore C50X codes alone could not be used to determine local recurrence. Codes representing breast and axillary lymph node excision within HES and Cancer Registry were used to identify possible treatment for local recurrence, providing the events happened 12 months after diagnosis (to avoid identifying the main local treatment normally given for primary disease). Considering these surgery codes on their own in HES, without a C50X code attached to the episode was not sufficient as a considerable amount of false positives were identified. Therefore, the patient had to have a C50X code at the same visit to better identify local recurrent events.\u003c/p\u003e\n \u003cp\u003eOnce the data needed to identify each event had been established a \u0026lsquo;final\u0026rsquo; set of programs was developed to streamline the procedure. The process map for disease-related event identification is summarised in Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003ePerformance of procedure\u003c/h2\u003e\n \u003cp\u003eA total of 1008 TTR events, 1547 iDFS events and 1124 deaths from a total of 9744 patients were available for detection from the three training trials combined; of which 917 (91.0%), 1460 (94.4%) and 1095 (97.4%) events respectively were correctly identified within RCHD. For the three key disease-related outcomes, agreement between trial events and NCRAS events was moderately good. Specificity was good across all endpoints (range: 97.9%-99.9% for three trials combined), as was NPV (range: 99.0%-99.7%), highlighting the low number of false positives from RCHD. Sensitivity and PPV were more variable across the trials with sensitivity ranging between 91.0%-97.4% for events detected at any timepoint and PPV ranging between 85.5%-99.5%. In all cases, these values were reduced when considering the validation dataset (FAST-Forward); however, the number of events available for detection for this trial was low. Similarly, sensitivity and PPV were reduced for all endpoints when considering events identified within a window of +/-26 weeks; a window that was considered appropriate in the context of long-term follow-up setting when contact with patients is often on an annual or bi-annual basis; this is broken down further to a window of +/- 12 weeks where the majority of events were identified (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). The variability in agreement measures, particularly sensitivity and PPV continues when considering the component events that individually make up TTR and iDFS with the poorest agreement found when trying to identify loco-regional recurrences (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003eTable 1: Level of agreement between NCRAS and trial data for each survival-related outcome\u003c/div\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1033\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e249\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e306\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e112\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e128\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e556\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e638\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e917\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1072\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e102\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e125\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e186\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e186\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e79\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e79\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e377\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e377\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e642\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e642\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e87\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e87\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e59\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e59\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e45\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e120\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e120\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e182\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e182\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n 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width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e2387\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e2401\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e48\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e2524\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e2572\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e91\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n 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colspan=\"3\"\u003e\n \u003cp\u003e1008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e8736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e9744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e3458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.764023210831722%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy \u0026ndash; overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.220502901353965%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n 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colspan=\"3\"\u003e\n \u003cp\u003e(76.6-85.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(82.1-93.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(80.5-92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(89.6-94.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(84.3-89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(89.0-92.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(83.3-87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(78.1-91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(73.7-88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.0-98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.9-99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.9-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.0-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(96.1-97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.5-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.9-98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.7-99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.0-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-8.7 (-13.9 to -3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"9\"\u003e\n \u003cp\u003e-9.4 (-14.1 to-4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-19.2 (-23.2 to -15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-15.1 (-18.0 to -12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"7\"\u003e\n \u003cp\u003e-3.3 (-5.3 to -1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.986460348162474%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.317214700193423%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e78.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e82.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(67.8-78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(72.7-83.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(66.9-82.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(77.6-91.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(68.4-75.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(80.7-87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(70.1-75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(79.9-85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(74.3-88.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(72.9-87.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.0-98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.5-98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.9-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.2-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(96.1-97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(92.8-94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.9-98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(96.5-97.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.0-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eiDFS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e543\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e61\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e604\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e158\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e171\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e759\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e855\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1460\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e170\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1630\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e194\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e32\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e226\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e456\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e456\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e121\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e121\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e554\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e554\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e1131\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e1131\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e173\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e173\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e67\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e67\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e110\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e110\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e61\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e61\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e138\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e138\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e219\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e219\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e8\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n 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colspan=\"3\"\u003e\n \u003cp\u003e8197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e9744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e3362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.58567279767667%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.679574056147144%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.679574056147144%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.001936108422072%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.001936108422072%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.679574056147144%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.325266214908035%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e89.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n 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width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(85.5-93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(80.6-90.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.7-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.6-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.1-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.0-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(95.2-96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.8-98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.6-98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.7-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.7-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e((99.0-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-2.2 (-5.1 to -0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"9\"\u003e\n \u003cp\u003e-6.3 (-9.9 to -2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-13.6 (-16.8 to -10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"9\"\u003e\n \u003cp\u003e-8.6 (-10.6 to -6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"7\"\u003e\n \u003cp\u003e-1.3 (-2.8 to 0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.986460348162474%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.317214700193423%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e83.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e77.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e80.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e88.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(80.1-86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(85.8-91.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(73.5-85.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(85.7-95.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(74.9-80.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(83.9-89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(78.1-82.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(86.1-89.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(81.2-90.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(79.9-89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.7-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(96.6-97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(99.1-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.0-99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(95.2-96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(91.9-93.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"3\"\u003e\n \u003cp\u003e(97.6-98.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"3\"\u003e\n \u003cp\u003e(95.9-96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"3\"\u003e\n \u003cp\u003e(98.7-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(98.8-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv\u003eTable 1: Level of agreement between NCRAS and trial data for each survival-related outcome (continued)\u003c/div\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1033\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e429\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e431\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e116\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e116\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e550\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e554\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1095\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e1101\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e137\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e137\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e428\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e428\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e116\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e116\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e548\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e548\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1092\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1092\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e136\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e136\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3557\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e3574\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2408\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2413\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2649\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2656\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n 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width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e3559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e4005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e2408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e2529\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e2653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" 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overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.679574056147144%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.679574056147144%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.001936108422072%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.001936108422072%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.3242981606969995%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.808325266214908%\" 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width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n 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width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(94.0-97.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.3-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(90.6-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(96.9-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(97.4-99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.2-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(96.3-98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.8-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(88.6-97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(97.3-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.6-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.5-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.9-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e0.2 (-0.1 to 0.4)\u003csup\u003ep\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"6\"\u003e\n \u003cp\u003e0.1 (-0.0 to 0.1)\u003csup\u003eI\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e0.1 (-0.1 to 0.3)\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"6\"\u003e\n \u003cp\u003e0.1 (-0.0 to 0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"5\"\u003e\n \u003cp\u003e0.8 (-0.7 to 2.3)\u003csup\u003eF\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.986460348162474%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.317214700193423%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(93.7-97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.3-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(90.6-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(96.9-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(97.2-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.2-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(96.1-98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(98.8-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(87.8-96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(97.3-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\" valign=\"top\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.6-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.4-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.5-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\" colspan=\"2\"\u003e\n \u003cp\u003e(99.9-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"30\" valign=\"top\"\u003e\n \u003cp\u003e\u003csup\u003eP\u0026nbsp;\u003c/sup\u003ePOETIC: Date of death doesn\u0026apos;t match n=30, 26 where two out of day, month and year match, 4 where only one out of day, month, year match.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eI\u0026nbsp;\u003c/sup\u003eIMPORT HIGH: Date of death doesn\u0026apos;t match n=10, 9 where two out of day, month and year match, 1 where none out of day, month, year match.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eT\u0026nbsp;\u003c/sup\u003eTACT2: Date of death doesn\u0026apos;t match n=32, 22 where two out of day, month and year match, 9 where only one out of day, month, year match, 1 where none out of day, month, year match.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eF\u0026nbsp;\u003c/sup\u003eFAST-Forward: Date of death doesn\u0026apos;t match n=11, 10 where two out of day, month and year match, 1 where only one out of day, month, year match\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable 2: Level of agreement between NCRAS and trial data for each event type\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1033\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDistant recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e275\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e93\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e108\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e499\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e559\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e832\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e110\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e942\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e88\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e173\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e173\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e68\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e68\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e329\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e329\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e570\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e570\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e70\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e70\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e63\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e63\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e101\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e43\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e43\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e107\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e107\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e161\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e161\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-13.8 (-18.8 to -8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e-10.1 (-15.3 to -5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-20.0 (-23.6 to -16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-17.1 (-19.8 to -14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e-5.5 (-8.9 to -2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e74.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e78.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e72.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e86.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e85.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e81.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(69.2-80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(79.6-89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(69.7-86.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(75.8-91.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(68.5-76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(83.2-89.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(70.9-76.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(83.3-88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(72.8-88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(72.8-88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e94.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e97.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.7-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(97.8-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.0-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(98.6-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(97.1-98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(93.7-95.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.5-99.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(97.0-97.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocal recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e23\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e112\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e165\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e60\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e81\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e81\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3941\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3971\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2495\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2510\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e110\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2988\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3098\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9424\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9579\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3536\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3552\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e2502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2529\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3210\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e9499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e37.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e46.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e56.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(14.2-42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(17.4-50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(25.5-64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(38.4-83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(30.7-45.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(50.1-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(30.7-43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(46.6-62.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(28.3-65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(34.9-75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.9-99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.0-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.0-98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(95.7-97.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.0-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.1-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.3-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-5.2 (-7.6 to -2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e-6.0 (-11.1 to -0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-3.0 (-6.0 to -0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-3.7 (-6.0 to -1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e-4.9 (-12.9 to 3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e26.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e44.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e36.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e59.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e35.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e54.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e40.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e52.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(14.2-42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(17.4-50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(25.5-64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(38.4-83.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(29.6-44.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(49.3-68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(29.9-42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(46.0-61.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(22.7-59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(30.6-73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e96.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.9-99.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.0-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.0-98.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(95.7-97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.0-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.1-98.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eTable 2: Level of agreement between NCRAS and trial data for each event type (continued)\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"1033\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast 2\u003csup\u003end\u003c/sup\u003e primary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e24\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e106\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e24\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e77\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e77\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e15\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3967\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3979\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2508\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2513\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3118\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3146\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9593\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9638\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3554\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3561\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e2510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2529\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3210\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e9619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy \u0026ndash; overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e65.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e64.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(49.0-81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(74.9-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(48.8-90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(61.7-98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(47.6-71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(52.7-77.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(54.9-72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(66.2-83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(45.1-86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(69.8-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.5-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.7-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.9-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.7-99.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.6-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.4-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.6-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e-0.2 (-0.8 to 0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e2.1 (0.7-3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e9.3 (0.2 to 18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e5.2 (0.4 to 10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e2.3 (0.6 to 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e66.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e55.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e63.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e61.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e74.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e93.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(49.0-81.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(74.9-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(48.8-90.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(61.7-98.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(43.3-67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(50.6-75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(52.5-70.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(65.2-82.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(45.1-86.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(69.8-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.5-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.7-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.9-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(98.6-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(99.6-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.3-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.8-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.6-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-breast 2\u003csup\u003end\u003c/sup\u003e primary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePOETIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMPORT HIGH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTACT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombined training trials\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eFAST-Forward\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e132\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e176\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e105\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e155\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e258\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e101\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e359\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e123\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e123\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e95\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e95\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e236\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e236\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e32\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent within 12-26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e12\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eEvent \u0026gt; 26 wks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo event\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3807\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3825\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2498\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2501\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n 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\u003cp\u003e\u003cstrong\u003e3520\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e2505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2529\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3210\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e9456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9744\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e3529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3577\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - overall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e75.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e90.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n 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\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(55.1-89.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(89.7-98.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(59.8-75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(86.5-93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(66.9-76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(58.2-84.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(47.6-74.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.5%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.5-99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.2-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.6-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(97.9-98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.6-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.7-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.6-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003eDifference in event dates, wks (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e1.2 (-0.5 to 3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e-6.8 (-24.4 to 10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e2.0 (0.2 to 3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.11798839458414%\" colspan=\"3\"\u003e\n \u003cp\u003e0.9 (-0.9 to 2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.665377176015475%\" colspan=\"3\"\u003e\n \u003cp\u003e6.3 (-0.9 to 13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"16\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy - within 26 weeks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e84.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e74.3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e79.2%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e73.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e92.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e67.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e87.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e71.1%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e68.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e60.0%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(77.3-89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(67.0-80.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(57.8-92.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(52.2-88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(86.2-96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(59.0-74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(82.6-90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(66.0-75.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(53.7-81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(45.9-73.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.8%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.7%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e\u003cstrong\u003e98.9%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.4%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e\u003cstrong\u003e99.6%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.315280464216634%\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.5-99.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.4-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(97.9-98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.5-99.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.996131528046422%\"\u003e\n \u003cp\u003e(98.7-99.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.802707930367505%\"\u003e\n \u003cp\u003e(99.5-99.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.1-99.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.673114119922631%\"\u003e\n \u003cp\u003e(99.3-99.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003eIn the example of TTR, further detail regarding mis-classifications for each individual event/non-event by trial is shown in Figures A2A-H. Considering distant recurrence in particular, where distant recurrence was reported in the trial data but not identified by the procedure in the NCRAS dataset (n\u0026thinsp;=\u0026thinsp;87); 6 (6.9%) patients with distant recurrence events across the four trials had a second primary cancer identified within NCRAS. Similarly, where the procedure identified a distant recurrence within NCRAS datasets that did not exist within the trial datasets (n\u0026thinsp;=\u0026thinsp;128), 26 (20.3%) had a second primary cancer recorded within the trial data.\u003c/p\u003e\n \u003cp\u003eAlmost always events were identified earlier in the trial data than in NCRAS due to identification of an event within NCRAS needing to be substantiated by collective activity rather than sporadic episodes of activity that might indicate a suspicion of recurrence. TTR events were, on average, identified 15.1 weeks earlier (95%CI:-18.0to-12.3) in the trial data than from NCRAS data (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Similarly, for iDFS the difference was 8.6 weeks earlier (95%CI: -10.6to-6.5) in the trial data than from NCRAS data. Similar differences between the trial data and NCRAS data were found for individual events (Table\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). As expected, there was no evidence of a difference in identifying death events between the trial data and NCRAS data (Table\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eDespite the challenges with matching, and the observed time delay in identifying events within NCRAS datasets, the survival estimates for the survival-related endpoints (TTR, iDFS and OS) for the individual RCTs were similar between NCRAS and trial data (Fig.\u0026nbsp;\u003cspan\u003e2\u003c/span\u003e). For example, 3-year estimates for TTR for POETIC were 94.5% (95%CI:93.8\u0026ndash;95.2) using NCRAS and 95.2% (95%CI:94.5\u0026ndash;95.8) within the trial. Similarly, HR for treatment effects were comparable between NCRAS and trial data indicating that any time differences for event detection were systematically applied across treatment groups (Tables A1 and A2). For example, HR for TTR in POETIC were 1.08 (95%CI:0.86\u0026ndash;1.37); p\u0026thinsp;=\u0026thinsp;0.51 for NCRAS and 1.10 (95%CI:0.86\u0026ndash;1.41); p\u0026thinsp;=\u0026thinsp;0.45 for trial data.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis work has shown that it is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis. Good levels of agreement were found for the composite endpoints tested and their individual component event-types. In addition, survival analyses showed that if NCRAS data were used in place of hospital-based follow-up, treatment effects would have been similar and conclusions drawn would have been the same for all four trials.\u003c/p\u003e \u003cp\u003eLittle work has been done to date around accuracy of disease-related endpoints such as TTR and iDFS within breast cancer and most of the data is not collected within the realms of a clinical trial and/or outside of the UK. An abstract by Mannu et al. in 2016 reported data from over 53,000 women in the West Midlands, UK, comparing recurrence information recorded in the National Cancer Registration Service in Birmingham to national RCHD datasets showed high agreement between these datasets with 92% of the patients having recurrence in either breast or lymph nodes, distant metastases or a second primary cancer identified within RCHD(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e); however this is comparing one local cancer registry who have a particular interest in breast cancer with national datasets and so is not directly comparable to our work comparing RCHD with data collected as part of a RCT. Outside of the UK, a Danish study by Rasmussen et al. in 2019(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), showed almost perfect agreement between Danish health registers and Danish Breast Cancer Group data for 471 women with early stage breast cancer, with a sensitivity of 97.3% (95%CI:93.2\u0026ndash;99.3), specificity of 97.2% (95%CI:94.8\u0026ndash;98.7), indicating that it is possible to achieve high accuracy in RCHD in well-curated datasets. In the US, Chubak et al.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) reported good sensitivity of 89% (95%CI:84\u0026ndash;92), specificity of 99% (95%CI:98\u0026ndash;99) and PPV of 90% PPV (95%CI:86\u0026ndash;94) comparing recurrences identified in SEER cancer registry data with medical record review in 3152 women with early stage breast cancer. In Canada, Jung et al.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) reported moderate-good agreement between RCHD and chart-reviewed breast cancer recurrence data for 598 patients with sensitivity of 94.2% and PPV of 79.2%. Notably, approximately two-thirds of their algorithm-estimated recurrence dates fell within 3 months of the chart-reviewed dates which is similar to the mean difference in times that we saw for this project.\u003c/p\u003e \u003cp\u003eElsewhere, in other cancer sites, some data are available comparing RCHD to either trial data or local source data with mixed results. A study by Mintz et al. using data from the STAMPEDE trial in patients with prostate cancer showed that a composite endpoint including elements from failure-free survival (FFS), metastases free survival (MFS) and PFS, developed using HES data, was comparable to the reported MFS endpoint in STAMPEDE \u0026ndash; HES composite endpoint HR\u0026thinsp;=\u0026thinsp;0.88 (95%CI: 0.77\u0026ndash;1.01) versus STAMPEDE MFS HR\u0026thinsp;=\u0026thinsp;0.82 (95%CI: 0.71\u0026ndash;0.95)(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). In addition, a study by Kelly et al. in 2017 showed acceptable agreement of 35/50 patients identified within routine data for PFS in Glioblastoma, however PFS estimation was less accurate than OS(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In head and neck cancer, Ricketts et al.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) calculated OS and PFS from routinely collected local data on inpatient admission and procedures, chemotherapy and radiotherapy and compared it with data collected directly from hospital notes for 122 patients. It showed good agreement for their automatic technique using RCHD versus hospital note data of 98% and 82% for OS and PFS respectively; although they noted that it underestimated recurrence rates due to lack of patients being treated for recurrent disease in this setting.\u003c/p\u003e \u003cp\u003eWithin our project, recurrences were most challenging to identify in approximately the first 15 months after diagnosis due to the overlap with the intensive treatment period for primary disease where treatment such as chemotherapy, anti-HER2 therapy and re-excisions of primary surgery would occur. This is a particular challenge in breast cancer subtypes at higher risk of early recurrence, such as triple negative breast cancer. Additional challenges with identifying local recurrences in particular are, if occurring in isolation, localised treatment (e.g. further surgery, radiotherapy) is not always well-recorded; or, if occurring around the time of a subsequent distant recurrence diagnosis, HES data will be dominated by systemic treatment for the metastases thus making the local recurrence hard to spot. Using RCHD in the long-term follow-up setting would avoid the overlap with the initial cancer treatment and events would be dominated by distant recurrence and, with increase age of trial participants, second primary cancers.\u003c/p\u003e \u003cp\u003eMisclassification of distant recurrences with second primary cancers, in addition, is a challenge when comparing trial data collected in \u0026ldquo;real-time\u0026rdquo; to routine data sourced later. The appearance of new disease, thought to be distant recurrence at the time and treated as such, may later be identified as a second primary cancer, e.g. via a post-mortem. It is also known that as patients get older, the incidence of second primary cancers increases; therefore, endpoints such as iDFS are robust to misclassification and arguably more relevant than TTR in the long-term follow-up setting when late effects of primary treatment, such as development of second primary cancers, need to be considered. However, with improving diagnosis methods and the increasing importance of biopsying metastatic disease to inform personalised treatment decisions, correct classification is likely to improve in future.\u003c/p\u003e \u003cp\u003eMore detailed coding, generally, within routine datasets would assist in identifying recurrences more easily. For example, when developing the procedure, it was found that the HES data that details the purpose of a visit to medical oncology was regularly incomplete therefore it was not truly possible to know if the patient was receiving cancer treatment. In addition to improved coding for the hospital episode itself, the use of ICD10 \u0026lsquo;C\u0026rsquo; codes would be beneficial alongside the treatment details provided within the medical oncology visit to help substantiate a cancer event.\u003c/p\u003e \u003cp\u003eThe majority of the recurrence events have been identified from key episodes in HES data but the definitive diagnosis date of the recurrence is not recorded so the episode date is used as a surrogate. In some cases this surrogate date may not be very accurate, for example, where a patient has received a treatment that may not be well documented in HES such as hormone therapy which does not require hospital admission. Our analysis showed that routine data frequently identified events later down the patient\u0026rsquo;s pathway; however, this difference did not impact significantly on overall analysis of survival outcomes.\u003c/p\u003e \u003cp\u003eWhilst we believe our procedure for identifying recurrences works, improvements can always be made. Upon manual inspection of the patients with no distant recurrence event identified within RCHD, 40/87 (46%) had treatment data within SACT and/or RTDS datasets that could have indicated a distant recurrence event; however, automating the use of this data field within these two datasets would have potentially increased the number of false positives identified and therefore needs to be balanced against this risk. Further work is planned to reduce the diagnosis date discrepancies identified between RCHD and trial data by making smarter use of SACT and RTDS data available to pick up additional treatment information and dates not recorded within HES and to add certainty/weight to the events found. In addition, consideration has been made to applying machine learning/AI methods to the datasets to maximise use of all available data, identifying the key fields for recurrence identification and improving the automation of this process; acknowledging the limitations of the currently available AI models.\u003c/p\u003e \u003cp\u003eA further limitation of our work is that the RCHD datasets used within this project are now quite old, although the majority of fields have remained the same. We had little data available from the COSD dataset at the time as it was relatively new and incomplete, similarly the SACT data was incomplete and a challenge to process. COSD specifically requests data on recurrence, updated data fields in 2019 include type of recurrence, so this dataset has the potential to replace existing identification practices if it has good completeness and data quality and will help ameliorate the challenge of identifying recurrence events close to initial diagnosis. This will need to be confirmed once the COSD dataset matures. Challenges with accessing RCHD in the UK, in terms of time, cost and data governance requirements over and above that already needed to run RCTs within the existing regulatory framework, in addition to the remaining data quality issues, are well-documented(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, using RCHD for long term follow-up as an alternative data source to hospital-based data collection is possible and with continual improvements in data quality, completeness and detail, it would indicate that current RCHD datasets would provide a suitable alternative to hospital-based data collection for long term follow-up of trial participants. The research community owe it to trial participants to ensure that the mechanisms to obtain such data are timely, feasible and affordable.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all four trials used within this research project patients had provided informed consent for access to routine medical records (POETIC: London\u0026ndash;South\u003c/p\u003e\n\u003cp\u003eEast Research Ethics Committee (08/H1102/37); TACT2: Scotland Multi-Research Ethics Committee (04/MRE00/88); IMPORT HIGH: Cambridgeshire\u003c/p\u003e\n\u003cp\u003e4 Research Ethics Committee (08/H0305/13); FAST-Forward: South East Coast Kent Research Ethics Committee (11/LO/0958)), therefore no additional approval was required to receive data from NHS England.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTrial data are available from the authors upon reasonable request as per ICR-CTSU data and sample access policy. \u0026nbsp;See website for further details: https://www.icr.ac.uk/our-research/centres-and-collaborations/centres-at-the-icr/clinical-trials-and-statistics-unit/working-with-us/data-sharing.\u003c/p\u003e\n\u003cp\u003eDatasets provided by NHS England were used with specific approval for this project thus restrictions apply to the availability of this data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRCHD datasets for each trial were kindly provided by NHS England (formally Public Health England) free of charge for use within this research project. \u0026nbsp; The ICR-CTSU receives core programme grant funding from Cancer Research UK (grant numbers C1491/A25351 and C1491/A15955) which supported this research project.\u003c/p\u003e\n\u003cp\u003eThis paper represents independent research part funded by the NIHR Biomedical Research Centre at the Royal Marsden NHS Foundation Trust and the Institute of Cancer Research. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLSK initiated, designed the project, analysed and interpreted the data and co-wrote the manuscript. \u0026nbsp;VH and SGM analysed and interpreted the data and co-wrote the manuscript. \u0026nbsp;JMB initiated, designed the project, analysed and interpreted the data and co-wrote the manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank our patients, the investigators and the research support staff at all participating centres, the trial management groups and trial steering committees for the four trials used for this research project. \u0026nbsp;We also thank staff at NHS England as data for this project includes information collected and quality assured by the NHS England National Cancer Registration and Analysis Service. Access to the data was facilitated by the PHE Office for Data Release, now part of NHS England.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ehttps://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer/survival; Accessed 17 July 2024.\u003c/li\u003e\n\u003cli\u003eEarly Breast Cancer Trialists\u0026apos; Collaborative Group (EBCTCG); Darby S, McGale P, Correa C, Taylor C, Arriagada R, Clarke M, Cutter D, Davies C, Ewertz M, Godwin J, Gray R, Pierce L, Whelan T, Wang Y, Peto R. Effect of radiotherapy after breast-conserving surgery on 10-year recurrence and 15-year breast cancer death: meta-analysis of individual patient data for 10,801 women in 17 randomised trials. Lancet. 2011 Nov 12;378(9804):1707-16.\u003c/li\u003e\n\u003cli\u003eEarly Breast Cancer Trialists\u0026apos; Collaborative Group (EBCTCG). Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet. 2005 May 14-20;365(9472):1687-717. \u003c/li\u003e\n\u003cli\u003eClarke M, Collins R, Darby S, Davies C, Elphinstone P, Evans V, Godwin J, Gray R, Hicks C, James S, MacKinnon E, McGale P, McHugh T, Peto R, Taylor C, Wang Y, Early Breast Cancer Trialists\u0026apos; Collaborative Group (EBCTCG). Effects of radiotherapy and of differences in the extent of surgery for early breast cancer on local recurrence and 15-year survival: An overview of the randomised trials. \u003cem\u003eLancet. \u003c/em\u003e2005 Dec 17;366(9503):2087\u0026ndash;106 \u003c/li\u003e\n\u003cli\u003eMintz HP, Dosanjh A, Parsons HM, Hughes A, Jakeman A, Pope AM, Bryan RT; BladderPath trial management group, James ND, Patel P. 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Lancet Digit Health. 2022 Aug;4(8):e567-e568.\u003c/li\u003e\n\u003cli\u003eLensen S, Macnair A, Love SB, Yorke-Edwards V, Noor NM, Martyn M, Blenkinsop A, Diaz-Montana C, Powell G, Williamson E, Carptenter J, Sydes MR\u003cem\u003e.\u003c/em\u003e Access to routinely collected health data for clinical trials \u0026ndash; review of successful data requests to UK registries. \u003cem\u003eTrials\u003c/em\u003e\u003cstrong\u003e21\u003c/strong\u003e, 398 (2020). \u003c/li\u003e\n\u003cli\u003eTolaney SM, Garrett-Mayer E, White J, Blinder VS, Foster JC, Amiri-Kordestani L, Hwang ES, Bliss JM, Rakovitch E, Perlmutter J, Spears PA, Frank E, Tung NM, Elias AD, Cameron D, Denduluri N, Best AF, DiLeo A, Baizer L, Butler LP, Schwartz E, Winer EP, Korde LA. Updated Standardized Definitions for Efficacy End Points (STEEP) in Adjuvant Breast Cancer Clinical Trials: STEEP Version 2.0. J Clin Oncol. 2021 Aug 20;39(24):2720-2731. \u003c/li\u003e\n\u003cli\u003eKilburn LS, Aresu M, Banerji J, Barrett-Lee P, Ellis P, Bliss JM. Can routine data be used to support cancer clinical trials? A historical baseline on which to build: retrospective linkage of data from the TACT (CRUK 01/001) breast cancer trial and the National Cancer Data Repository. Trials. 2017 Nov 23;18(1):561.\u003c/li\u003e\n\u003cli\u003eSmith I, Robertson J, Kilburn L, Wilcox M, Evans A, Holcombe C, Horgan K, Kirwan C, Mallon E, Sibbering M, Skene A, Vidya R, Cheang M, Banerji J, Morden J, Sidhu K, Dodson A, Bliss JM, Dowsett M. Long-term outcome and prognostic value of Ki67 after perioperative endocrine therapy in postmenopausal women with hormone-sensitive early breast cancer (POETIC): an open-label, multicentre, parallel-group, randomised, phase 3 trial. 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Dose-escalated simultaneous integrated boost radiotherapy in early breast cancer (IMPORT HIGH): a multicentre, phase 3, non-inferiority, open-label, randomised controlled trial. Lancet. 2023 Jun 24;401(10394):2124-2137. \u003c/li\u003e\n\u003cli\u003eCameron D, Morden JP, Canney P, Velikova G, Coleman R, Bartlett J, Agrawal R, Banerji J, Bertelli G, Bloomfield D, Brunt AM, Earl H, Ellis P, Gaunt C, Gillman A, Hearfield N, Laing R, Murray N, Couper N, Stein RC, Verrill M, Wardley A, Barrett-Lee P, Bliss JM; TACT2 Investigators. Accelerated versus standard epirubicin followed by cyclophosphamide, methotrexate, and fluorouracil or capecitabine as adjuvant therapy for breast cancer in the randomised UK TACT2 trial (CRUK/05/19): a multicentre, phase 3, open-label, randomised, controlled trial. Lancet Oncol. 2017 Jul;18(7):929-945. \u003c/li\u003e\n\u003cli\u003eMannu GS, Broggio J, Charman J, Darby S. Identifying recurrence in breast cancer patients from routinely collected data in England. Eur J Surg Oncol. 2016; 42(5): PS33-S34.\u003c/li\u003e\n\u003cli\u003eRasmussen LA, Jensen H, Virgilsen LF, Thorsen LBJ, Offersen BV, Vedsted P. A validated algorithm for register-based identification of patients with recurrence of breast cancer\u0026mdash;Based on Danish Breast Cancer Group (DBCG) data. Cancer Epidem.,2019; 59: 129-134.\u003c/li\u003e\n\u003cli\u003eChubak J, Yu O, Pocobelli G, Lamerato L, Webster J, Prout MN, Ulcickas Yood M, Barlow WE, Buist DS. Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer. J Natl Cancer Inst. 2012; Jun 20;104(12):931-40. \u003c/li\u003e\n\u003cli\u003eJung H, Lu M, Quan ML, Cheung WY, Kong S, Lupichuk S, Feng Y, Xu Y. New method for determining breast cancer recurrence-free survival using routinely collected real-world health data. BMC Cancer 2022; 22: 281.\u003c/li\u003e\n\u003cli\u003eMintz HP. Can routinely collected data be used to inform randomised controlled trial outcomes in oncology. University of Warwick, 2019.\u003c/li\u003e\n\u003cli\u003eKelly C, Majewska P, Ioannidis S, Raza MH, Williams M. Estimating progression-free survival in patients with glioblastoma using routinely collected data. J Neurooncol. 2017 Dec;135(3):621-627.\u003c/li\u003e\n\u003cli\u003eRicketts K, Williams M, Liu ZW, Gibson A. Automated estimation of disease recurrence in head and neck cancer using routine healthcare data. Comput Methods Programs Biomed. 2014 Dec;117(3):412-24. \u003c/li\u003e\n\u003cli\u003eMacnair A, Love SB, Murray ML, Gilbert DC, Parmar MKB, Denwood T, Carpenter J, Sydes MR, Langley RE, Cafferty FH. Accessing routinely collected health data to improve clinical trials: recent experience of access. Trials 22, 340 (2021).\u003c/li\u003e\n\u003cli\u003eAppleyard SE, Gilbert DC. Innovative Solutions for Clinical Trial Follow-up: Adding Value from Nationally Held UK Data. Clinical Oncol. 2017; 29(12): 789-795.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cancer trials, routine data linkage, recurrence","lastPublishedDoi":"10.21203/rs.3.rs-4780757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4780757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: With continued improvements in breast cancer (BC) outcomes and risk of recurrence occurring until at least 20 years post-diagnosis, it is important to continue to follow-up clinical trial participants to characterise long-term treatment impact. Traditionally follow-up has been via hospitals; entailing burden on patients and site-staff. Using routinely collected health datasets (RCHD) as an alternative method is attractive, but historically cancer recurrence is poorly recorded unlike initial cancer diagnosis. Here we use data collected prospectively from large, multi-centre BC clinical trials to develop and test a procedure to identify recurrence within RCHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data from four trials of early breast cancer (TACT2, POETIC, IMPORT-HIGH and FAST-Forward) where recurrence data has been collected prospectively (gold standard) was linked with RCHD (incl. cancer registry and hospital episode statistics; HES) managed by NHS England. The procedure identified episodes of clinical activity within RCHD to classify each event type (local and distant recurrence, second cancers, death) separately then combined to derive time-to-recurrence (TTR), disease-free survival (iDFS) and overall survival (OS) outcomes. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Hazard ratios using Cox regression modelling, log rank test p-values and three-year survival-rates for the randomised treatments were reported separately for RCHD and trial data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final procedure used Cancer Registry diagnoses to identify initial BCs for quality control purposes and second primary cancers. Deaths were identified via death dates and cause. Distant recurrence was identified predominately by direct indicators of metastases (e.g. ICD10 codes C77X-79X). Local recurrence was identified via relevant surgeries’ OPCS4 codes. For TTR, iDFS and OS, agreement between study and RCHD events was reasonable. Specificity was good across all endpoints (range:97.9%-99.9% for three training datasets combined), as was NPV (range:95.2%-99.6%). Sensitivity and PPV were more variable with sensitivity ranging between 72.9%-97.2% and PPV ranging between 82.6%-99.5%. Values were similar when considering the test dataset. Survival estimates for TTR, iDFS and OS were similar between study and RCHD data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003eIt is possible, with reasonable accuracy, to identify cancer recurrences using RCHD in the place of hospital-based data collection after the point of primary analysis.\u003c/p\u003e","manuscriptTitle":"Use of routinely collected health data (England) to identify subsequent disease-related events in patients with primary breast cancer: A practical alternative to hospital-based follow-up for breast cancer clinical trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-08 19:01:38","doi":"10.21203/rs.3.rs-4780757/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-06-01T11:29:11+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-04-17T09:40:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-31T12:11:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-31T10:33:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trials","date":"2024-07-22T05:01:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dd8c117f-552e-431d-b54c-e197e1678d9c","owner":[],"postedDate":"September 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:06:19+00:00","versionOfRecord":{"articleIdentity":"rs-4780757","link":"https://doi.org/10.1186/s13063-025-09085-1","journal":{"identity":"trials","isVorOnly":false,"title":"Trials"},"publishedOn":"2025-09-26 15:58:21","publishedOnDateReadable":"September 26th, 2025"},"versionCreatedAt":"2024-09-08 19:01:38","video":"","vorDoi":"10.1186/s13063-025-09085-1","vorDoiUrl":"https://doi.org/10.1186/s13063-025-09085-1","workflowStages":[]},"version":"v1","identity":"rs-4780757","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4780757","identity":"rs-4780757","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}