Discrepancies in reporting of study design and analysis methods between protocols and reports of interrupted time series (ITS) studies: a methodological study

Discrepancies in reporting of study design and analysis methods between protocols and reports of interrupted time series (ITS) studies: a methodological study | 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 Discrepancies in reporting of study design and analysis methods between protocols and reports of interrupted time series (ITS) studies: a methodological study Phi-Yen Nguyen Nguyen, Elizabeth Korevaar, Matthew J. Page, Joanne E. McKenzie, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8612197/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background : Unexplained changes from a study’s analysis plan may increase the risk of bias in study results and undermine confidence in the findings. Discrepancies in methods between protocols and reports have not been examined in interrupted time series (ITS) studies. Methods : We searched for peer-reviewed protocols of ITS studies in 22 databases, and results reports addressing each protocol’s primary research question. We compared 20 design and analysis items between protocols and their reports, classifying an item as ‘discrepant’ when the report provided different details about the item compared to the protocol. We judged a discrepancy as ‘potentially important’ if it could have potentially impacted the results or the study conclusion. We recorded if authors provided justifications for discrepancies. Results : From 4,590 abstracts, after excluding ongoing studies, we identified 120 eligible ITS protocols, from which 44 protocols (37%) had at least one corresponding results report. Information about handling the complexities of time-series data were frequently missing from the protocol or report, or both, for example, methods of adjusting for autocorrelation (77% of studies, 34/44) and seasonality (82%, 36/44). Potentially important discrepancies were common for eligibility criteria (43%, 19/44), overall length of time series (39%, 17/44), length of each time segment (45%, 20/44), effect measures (25%, 11/44) and ITS analysis methods (23%, 10/44). Among studies with important discrepancies, justifications were missing in 50%-100% of cases. Conclusion : Study designs and analysis methods were often not, or insufficiently, reported in ITS studies. Changes to original analysis plans were also prevalent and often unjustified, precluding readers from judging the legitimacy of the changes. Protocols for ITS studies should provide detailed information about the design and analysis methods. Deviations from planned methods should be transparently reported with clear justifications. interrupted time series reporting bias statistical methods methodology statistical analysis data collection study design non-randomised studies discrepancies deviations Figures Figure 1 Figure 2 BACKGROUND An interrupted time series (ITS) study is a non-randomised design that can be characterised by the collection of data continuously over time, aggregation of this data using summary statistics (such as proportions) within regular time intervals (e.g., monthly), and separation of the resulting time series into segments by ‘interruption(s)’. The interruptions can be an exposure (e.g., disease outbreak) or an intervention (e.g., implementation of a national policy). The design can reduce the risk of selection and confounding biases caused by inter-group differences when using a control group ( 1 ). This makes the ITS design a robust alternative when randomisation may be unethical, infeasible or impractical, such as when evaluating interventions targeted at populations ( 2 – 4 ). Many model structures and analysis methods may be used to analyse the ITS design. Commonly, a segmented regression model is fitted, which involves estimating the underlying time trend in the pre-interruption segment and extrapolating this into the post-interruption segment. This extrapolated trend provides a “counterfactual” for what would have occurred in the absence of the interruption. The impact of the interruption can then be estimated by comparing the counterfactual time trend with what was observed, using the estimated post-interruption time trend ( 1 , 5 , 6 ). From this comparison, a number of different effect measures can be calculated (e.g., immediate level change, change in slope). The model can be extended, and different analysis methods can be used, to handle issues associated with time series data, such as autocorrelation, seasonality, and time-varying confounders ( 6 – 8 ). A protocol is a document that details the design and analysis methods for a research study. Protocols are an ethical principle for medical research involving human participants as stated in the World Medical Association’s Declaration of Helsinki ( 9 ). Writing a protocol before conducting a research is highly encouraged in journal policies ( 10 – 12 ) and reporting guidelines for randomised controlled trials (RCTs) and systematic reviews ( 13 – 15 ). Protocols guide research teams in applying the methods, allow design improvements through peer review, and protect against biases arising from selective reporting of outcomes and inappropriate changes to the design and analysis methods. Perhaps inevitably, however, unanticipated circumstances arise that can lead to the need to deviate from the planned methods. Reporting of these deviations, along with why and when they occurred, allows users of the research to judge the potential for any risks of bias. Examination of discrepancies in methods between protocols and results reports has been extensively investigated for randomised trials ( 16 – 18 ) and systematic reviews ( 19 , 20 ), and have revealed substantial discrepancies in primary analysis methods and eligibility criteria ( 16 , 21 – 23 ). We are unaware of any such investigation of ITS studies. Given the importance of ITS studies in providing evidence for policy decisions, such an investigation is required. OBJECTIVE To examine discrepancies in the reporting of design and analysis methods between ITS protocols and their corresponding results reports. METHODS This study is part of a series of three studies investigating reporting biases among ITS studies, for which we have published a protocol ( 24 ) (Additional File 1). Here, we provide an overview of the methods and results for the third study; those of other studies are published separately. Deviations from the methods outlined in the protocol are available in Additional File 2. 1. Creating a database of ITS studies We searched for published, peer-reviewed protocols of ITS studies from 22 databases, using a highly sensitive search filter ( 25 ). For each protocol, we identified the “primary research question”, as stated by authors, or if not specified as primary, the first ITS research question listed in the aims or methods sections. We then searched the literature for the corresponding results reports that addressed the primary ITS research question from the protocol (see Additional Files S3 and S4 for details on literature search, screening and determining the primary research question). If there were multiple reports of results for a particular study, we included only the primary report, that is, the report that addressed the protocol’s primary ITS research question and most closely matched the study design in the protocol. ITS studies for which both a protocol and report of results were found constituted the sample for this study. 2. Identifying and extracting data From each protocol and the report of results, we extracted information on study design and analysis methods across four areas: Study design: the primary research question, eligibility criteria and data sources (labelled items A to C in Fig. 2 and Table 3 ); Table 3 Percentage of discrepancies between protocols and results reports Item Discrepancy (%) Potentially important discrepancy a (%) Justification not provided for discrepancy (%) Overview of study design (A) Primary research question Missing information in protocol and/or results report 0/44 (0%) - - Any discrepancy between results report and protocol 13/44 (30%) 10/44 (23%) 10/10 b (100%) Cannot be determined c 0/44 (0%) - - (B) Eligibility criteria Missing information in protocol and/or results report 2/44 (5%) - - Any discrepancy between results report and protocol 31/44 (70%) 19/44 (43%) 19/19 (100%) Cannot be determined 0/44 (0%) - - (C) Data sources Missing information in protocol and/or results report 0/44 (0%) - - Any discrepancy between results report and protocol 20/44 (45%) 10/44 (23%) 9/10 (90%) Cannot be determined 2/44 (5%) - - Characteristics of the time series (D) Overall length of the time series Missing information in protocol and/or results report 9/44 (20%) - - Any discrepancy between results report and protocol 22/44 (50%) 17/44 (39%) 13/17 (76%) Cannot be determined 4/44 (9%) - - (E) Start and end dates of each segment in the time series Missing information in protocol and/or results report 20/44 (45%) - - Any discrepancy between results report and protocol 17/44 (39%) 12/44 (27%) 8/12 (67%) Cannot be determined 2/44 (5%) - - (F) No. data points in each segment in the time series Missing information in protocol and/or results report 7/44 (16%) - - Any discrepancy between results report and protocol 27/44 (61%) 20/44 (45%) 14/20 (70%) Cannot be determined 1/44 (2%) - - The ITS model (G) Start and end dates of each segment in the ITS model Missing information in protocol and/or results report 22/44 (50%) - - Any discrepancy between results report and protocol 15/44 (34%) 12/44 (27%) 7/12 (58%) Cannot be determined 2/44 (5%) - - (H) No. data points in each segment in the ITS model Missing information in protocol and/or results report 12/44 (27%) - - Any discrepancy between results report and protocol 22/44 (50%) 17/44 (39%) 13/17 (76%) Cannot be determined 2/44 (5%) - - (I) Time interval(s) at which outcome data was aggregated Missing information in protocol and/or results report 6/44 (14%) - - Any discrepancy between results report and protocol 8/44 (18%) 8/44 (18%) 7/8 (88%) Cannot be determined 5/44 (11%) - - (J) How the interruption was modelled Missing information in protocol and/or results report 7/44 (16%) - - Any discrepancy between results report and protocol 5/44 (11%) 3/44 (7%) 3/3 (100%) Cannot be determined 3/44 (7%) - - (K) Which segments were compared to address the primary research question Missing information in protocol and/or results report 6/44 (14%) - - Any discrepancy between results report and protocol 4/44 (9%) 3/44 (7%) 3/3 (100%) Cannot be determined 5/44 (11%) - - (L) Types of effect measures reported Missing information in protocol and/or results report 13/44 (30%) - - Any discrepancy between results report and protocol 14/44 (32%) 11/44 (25%) 11/11 (100%) Cannot be determined 3/44 (7%) - - Statistical analysis methods (M) ITS analysis method(s) Missing information in protocol and/or results report 14/44 (32%) - - Any discrepancy between results report and protocol 14/44 (32%) 10/44 (23%) 9/10 (90%) Cannot be determined 10/44 (23%) - - (N) Decision rule on whether to adjust for autocorrelation Missing information in protocol and/or results report 34/44 (77%) - - Any discrepancy between results report and protocol 4/44 (9%) 2/44 (5%) 2/2 (100%) Cannot be determined 0/44 (0%) - - (O) Method(s) of testing for autocorrelation Missing information in protocol and/or results report 30/36 d (83%) - - Any discrepancy between results report and protocol 3/36 (8%) 3/36 (8%) 2/3 (67%) Cannot be determined 0/36 (0%) - - (P) Method(s) of adjusting for autocorrelation Missing information in protocol and/or results report 34/44 (77%) - - Any discrepancy between results report and protocol 2/44 (5%) 1/44 (2%) 1/1 (100%) Cannot be determined 1/44 (2%) - - (Q) Method(s) of testing & adjusting for seasonality Missing information in protocol and/or results report 36/44 (82%) - - Any discrepancy between results report and protocol 5/44 (11%) 3/44 (7%) 3/3 (100%) Cannot be determined 0/44 (0%) - - (R) Method(s) of testing & adjusting for non-stationarity Missing information in protocol and/or results report 41/44 (93%) - - Any discrepancy between results report and protocol 0/44 (0%) 0/44 (0%) - Cannot be determined 0/44 (0%) - - (S) Presence and type of control series Missing information in protocol and/or results report 2/44 (5%) - - Any discrepancy between results report and protocol 7/44 (16%) 5/44 (11%) 4/5 (80%) Cannot be determined 1/44 (2%) - - (T) Method(s) of comparing intervention and control series Missing information in protocol and/or results report 9/20 e (45%) - - Any discrepancy between results report and protocol 3/20 (15%) 2/20 (10%) 1/2 (50%) Cannot be determined 3/20 (15%) - - Notes: Three types of discrepancies were recorded : ( 1 ) results report details did not match protocol; ( 2 ) results report had fewer details than protocol; ( 3 ) results report had more details than protocol. See Additional File 10 for a breakdown of these three types of discrepancies. a Discrepancy had potential to significantly impact the results. See Additional File 6 for examples. b Denominator is the number of studies with important discrepancy between the protocol and the results report. c “Cannot be determined” is applicable to studies that had some information about the item reported in both the protocol and the results report, but the information was either too vague or insufficient to determine whether there was a discrepancy, or what the type of discrepancy was. d For “Method(s) of testing for autocorrelation”, the denominator only includes studies where the authors said they might test for presence of autocorrelation. e For “Method(s) of comparing intervention and control series”, the denominator only includes studies where there was a control series. Abbreviations: ITS: interrupted time series Characteristics of the time series: overall length in terms of number of data points, length of each segment, and start and end date of each segment (items D to F); ITS model: length of each segment and start and end date of each segment based on model parameters (items G and H), the time intervals at which data were aggregated, how the interruption was modelled, the segments to be compared, and the types of effect measures generated (items I to L); Statistical methods: ITS analysis methods (e.g., ordinary least squares [OLS], Prais-Winsten generalised least squares [GLS]), item M), methods to detect and handle autocorrelation, seasonality and non-stationarity (items N-R), presence of control series and methods to compare intervention with control series (items S to T). To pilot the data extraction form (Additional File 5), all five authors (PYN/JEM/SLT/EK/MJP) independently extracted data from the same five studies to ensure a shared understanding of the process. Data from the remaining studies were extracted by two reviewers (PYN and one of JEM/SLT/EK/MJP). Any difference in extracted data was resolved via discussion between the reviewers or at regular team meetings. 3. Assessing discrepancies in methods between protocols and reports of results For each item, two reviewers assessed whether there was any discrepancy between what was reported in the protocol and the report of results. Each item was classified into one of the following categories: Discrepancy categories: (1) Report of results had fewer details than protocol: the protocol had some information about the item that was not mentioned in the report of results, but the provided details about the item were aligned. (2) Report of results had more details than protocol: the report of results had some information about the item that was not mentioned in the protocol, but the provided details about the item were aligned. (3) Report of results’ details did not match the protocol’s: some information about the item was provided in both documents but the information was not aligned. No discrepancy category: (4) Report of results’ details matched those from the protocol: both documents provided a similar level of detail about the item and all the details were consistent. Not assessed for discrepancies: (5) Cannot be determined: some information was provided about the item in both the protocol and report of results, but there was insufficient detail to enable a judgement of (1) to (4). (6) Missing information in protocol, report of results, or both: no information was provided at all about the item in either of the documents, or both. Categories (1)-(3) were considered discrepancies while category (4) was not considered a discrepancy. Categories (5) and (6), were not assessed for discrepancy, due to insufficient information in one or both of the documents (see Table 1 for examples). Table 1 Assessing discrepancies in reporting of item D – Overall length of the time series| Examples for each type of discrepancy E.g., As reported in protocol As reported in results report Rationale for our discrepancy assessment Assessment of discrepancy 1 No information available A figure shows a plot of the ITS with 24 data points. - Not assessed for discrepancy : Missing information in protocol 2 The equation of the ITS model describes: “ t is the time in months from the start of the observation period, ranging from 1 to 22 months”. No information available From the protocol, it could be inferred that there were 22 data points (22 months of monthly data). Not assessed for discrepancy : Missing information in results report 3 No information available “We will use ITS analyses to assess changes in healthcare attendances from 2013 to 2016” From the results report, the time interval for data aggregation was not reported, so we could not deduce the number of data points between 2013 and 2016. Not assessed for discrepancy : Missing information in both protocol and results report 4 "The ITS will include routinely collected data spanning July 2007 to January 2012, providing 45 pre- and 10 post-intervention data points.” A figure shows a plot of the ITS with 55 data points. From the protocol, the total number of data points summed to 55, which is consistent with the number of data points shown in the figure. No discrepancy : Results report details matched with the protocol 5 A figure shows individual plots of ITS for three intervention sites, showing 30, 32 and 30 data points, respectively. "The ITS analysis had 12 data points in the pre-implementation period and 18 data points in the post-implementation periods”. The results report did not provide as much detail as the protocol in providing the number of data points for all sites. Discrepancy : Results report had fewer details than protocol 6 “Each data set will cover at least 2 years of data on monthly population-based vaccination rates on either side of vaccine introduction”. “We analysed monthly population-based vaccination rates using 27 months of data before and 45 months of data after the introduction of the vaccine”. The protocol suggests there will be at least 48 data points, which is consistent with what was reported in the results report (72 data points) but not an exact match. Discrepancy : Results report had more details than protocol 7 “The ITS consists of two time periods: 12 months of preintervention (3 data points) and 12 months of postintervention (3 data points).” "The data points for the time series data represent the proportion of patients hospitalised per two months (i.e., six data points before and six data points after the intervention each consisting of at least 30 patients)" The numbers of data points were different due to different time intervals of aggregation. Discrepancy : Results report details did not match the protocol 8 “The study will be conducted in three phases: pre-implementation (January to March 2023); implementation (April 2023 to March 2024 planned); and maintenance (April 2024 to March 2025).” “Pre-implementation and implementation activities were carried out between January 2023 and March 2024, followed by a maintenance period up to June 2025”. In both the protocol and the results report, the time intervals of the data were not reported, so the exact number of data points could not be deduced. However, the end dates of the maintenance period were clearly different, so it is highly likely that the total number of data points would have differed as well. Discrepancy : Results report details did not match the protocol 9 “Monthly data on contraceptive use is recorded from six months prior to baseline and every month until end-line (at 24 months after baseline)." Text: “31 months of data were collected from September 2017 to March 2020”. Figure shows a plot of the ITS commencing in August 2017 and ending in January 2020. There was information in the protocol to determine the number of data points (30 months x monthly data = 30 data points). However, in the results report, the number of data points could be either 31 (based on text) or 30 (based on figure). Hence, it is impossible for the reviewers to decide whether there was a discrepancy or not. Not assessed for discrepancy : Cannot be determined Notes: (a) The text within quotation marks is text extracted from the included ITS protocols and results reports (modified for confidentiality). All other text is the reviewer’s remarks/observations. (b) We considered all information irrespective of where it was reported (e.g., figures, equations, tables, text). If there were discrepancies between different sources of information within a protocol or within a results report, two independent reviewers discussed to select the most reliable information, giving priority to the information that was supported by more than one sources. If it was impossible to decide based on the available information, we did not assess for discrepancy (see example 9). Abbreviations: ITS: interrupted time series We judged the potential importance of each discrepancy (i.e., whether it would potentially impact the results of the analysis or the conclusion of the study, see Additional File 6 for examples). For each potentially important discrepancy, we extracted verbatim any rationale for the deviation provided by the authors. All assessments were undertaken independently by pairs of reviewers (PYN/MJP or EK/SLT). Differences in assessment were discussed by the pairs to reach consensus. Finally, PYN checked that the decision rules were applied consistently across all studies. 4. Statistical analysis We calculated the frequency of reporting for each item of the study design and analysis methods. For each item, we calculated the percentage of studies with: any discrepancy [categories ( 1 )-( 3 )]; any potentially important discrepancy; and any potentially important discrepancy with a justification. We categorised the reported justifications for the discrepancies. RESULTS 1. Overview of the search process We screened 4,590 abstracts and the full text of 148 ITS study protocols. After excluding protocols that did not meet inclusion criteria and ongoing studies, 120 ITS study protocols were selected. A search for corresponding reports of results identified 467 potentially eligible full texts, from which 44 met our inclusion criteria. Of the 120 ITS study protocols, 76/120 (63%) did not have a matching report of results, while 44/120 (37%) had at least one matched report of results. These 44 pairs of protocols and corresponding reports of results constituted the sample for this study (Fig. 1 ). 2. Characteristics of included studies The ITS protocols were published between 2010 and 2022 (Table 2 ). Most studies (33/44, 75%) outlined methods for other study designs (e.g., qualitative analysis) in addition to the ITS study analysis. Half of the studies (22/44, 50%) were registered. Most studies (37/44, 84%) were supported by non-industry funding and most (36/44, 82%) were conducted in a high-income country ( 26 ). In the results reports of these studies, the median number of data points in the time series was 36 (IQR 24 to 70) and aggregation of outcome data to monthly intervals was most common (26/44, 59%). (see Additional File 7 for further characteristics of the design and analysis methods). Table 2 Characteristics of included studies Characteristics No. ITS studies (%) (N = 44) Focus of the study Only ITS study 11 (25%) ITS and other study designs 33 (75%) Type of funding No funding 1 (2%) Non-industry funding 37 (84%) Industry funding 6 (14%) Study registration 22 (50%) Nature of the interruption Exposure (natural events) 0 (0%) Intervention 44 (100%) Practice change in a clinical setting 25 (57%) Health system interventions 8 (18%) Policy & regulatory changes 6 (14%) Social & economic interventions 3 (7%) Environmental interventions 2 (5%) Level at which the intervention was implemented Unit-based or institutional 18 (41%) Regional 8 (18%) National 17 (39%) Multinational 1 (2%) Level at which the intervention was evaluated Unit-based or institutional 21 (48%) Regional 10 (23%) National 12 (27%) Multinational 1 (2%) Country where study was conducted † High-income countries 36 (82%) Upper middle-income countries 3 (7%) Lower middle-income countries 4 (9%) Low-income countries 1 (2%) Timing of data collection relative to the protocol’s submission Retrospective 17 (39%) Prospective 27 (61%) Notes: For details of how we defined the options for each characteristic, refer to Additional File 5 (Data extraction form). Abbreviation: ITS: interrupted time series † Based on World Blank Group’s FY25 income classification. Total of percentages may exceed 100% as multiple response options could apply. 3. Completeness of reporting (Table 3 & Fig. 2) 3.1. Items related to study design The primary research question, eligibility criteria and data sources (items A-C) were commonly reported, with only 2/44 studies (5%) having missing information about eligibility criteria and 2/44 studies (5%) not reporting sufficient detail about the data sources for discrepancy assessment. 3.2. Items related to characteristics of time series The overall length of the time series (item D) and number of data points of each segment (item F) in the time series were commonly reported, with a fifth of the studies missing information about these items (9/44 studies [20%] for item D and 7/44 [16%] for item F). However, almost half of the studies (20/44 [45%]) had missing information on the start or end date of each time segment (item E). For these items, the number of studies that did not report sufficient information for discrepancy assessment was less than five. 3.3. Items related to the ITS model Information was missing on start and end date of the modelled time series (item G) in half of the studies (22/44 [50%]), and on number of data points of each modelled segment (item H) in a third of studies (12/44 [27%]). A smaller percentage of studies had missing information on the time intervals for outcome aggregation (item I, 6/44 studies [14%]), how the interruption was modelled (item J, 7/44 [16%]), and the segments to be compared (item K, 6/44 [14%]). Almost a third of studies (13/44 studies [30%]) had missing information about the type of effect measure(s) (item L) to be reported, and in a further 7%, the effect measures could not be determined. For each item, the number of studies that did not report the information sufficient for discrepancy assessment was five or fewer. 3.4. Items related to statistical methods The ITS analysis methods (item M) were not commonly reported, with a third of the studies (14/44 [32%]) not reporting the methods and another quarter of the studies (10/44 [23%]) providing insufficient detail to allow for a discrepancy assessment. In a large percentage of studies, the authors did not report on whether they would use a decision rule to adjust for autocorrelation (item N, 32/44 [77%]) or the method of adjusting for autocorrelation (item P, 34/44 [77%]). Among the studies where the authors stated they would test for the presence of autocorrelation, the method of testing (item O) was not reported in 30/36 studies (83%). Similarly, information was frequently missing on the methods for adjusting for seasonality (item Q, 36/44 studies [82%]) and stationarity (item R) (41/44 [93%]). In most studies, the presence (or not) of a control series was commonly reported (item S), with only 2/44 studies (5%) not reporting this information. However, among 20 studies with control series, 9 (45%) reported no information on the methods for comparing intervention series with control series (item T), and 3 (15%) had insufficient information to assess discrepancies for this item. 4. Discrepancies in reporting (Table 3 & Fig. 2) In this section, we report the frequency of discrepancies between protocols and results reports. Specific details of the design and analysis methods reported in the protocols and the results reports are available in Additional File 7. 4.1. Items related to study design A quarter of the studies (10/44, 23%) had potentially important discrepancies in the primary research question (item A) or data sources (item C), with all or most of these discrepancies not justified (100% and 90%, respectively). Similarly, 19/44 studies (43%) had potentially important discrepancies in eligibility criteria (item B), all of which were unjustified. 4.2. Items related to characteristics of time series A high percentage of studies had potentially important discrepancies in reporting the overall length of the time series (item D, 17/44 studies [39%]), start and end dates of each segment (item E, 12/44 [27%]) and the number of data points in each segment in the time series (item F, 20/44 [45%]). For each item, greater than two-thirds of the discrepancies were not justified (13/17 studies [76%] for item D, 8/12 [67%] for item E, and 14/20 [70%] for item F). 4.3. Items related to the ITS model The patterns of potentially important and unjustified discrepancies in the length (item H), and start and end dates of each segment (item G) used in the ITS model mirrored what was observed in the design of the time series. A smaller percentage of studies had potentially important discrepancies in the time intervals for outcome aggregation (item I, 8/44 studies [18%]), how the interruption was modelled (item J, 3/44 [7%]), and the segments to be compared (item K, 3/44 [7%])). However, most or all of these discrepancies were unjustified (7/8 studies [88%] for item I, 3/3 [100%] for item J, and 3/3 [100%] for item K). A quarter of studies had potentially important discrepancies in the types of effect measures reported (item L, 11/44 studies [25%]), all of which were unjustified. 4.4. Items related to statistical methods A quarter of studies (10/44 [23%]) had potentially important discrepancies in ITS analysis methods (item M), with nearly all being unjustified (9/10 studies [90%]). Autocorrelation, seasonality and non-stationarity were rarely mentioned (items N-R), resulting in very few studies where we could compare and assess for discrepancies in the methods to handle them. However, most or all of the potentially important discrepancies (67% to 100%) were not justified. A small percentage of studies had potentially important discrepancies in the presence (or absence) of control series (5/44 [11%], item S). Among studies with control series, 2/20 studies [10%] had potentially important discrepancies in the methods of comparing intervention series with control series (item T), with one discrepancy unjustified. 5. Justifications for discrepancies In 10/44 studies (23%), the authors provided a justification for at least one potentially important discrepancy (Additional File 8). Some justifications reflected factors outside of the investigators control such as impacts of COVID-10 restrictions, lack of funding, or inability to obtain data from control sites. Other justifications reflected changes made by the investigators such as changes to statistical methods to deal with unforeseen problematic data. For example, in one study, the authors changed the time intervals of outcome aggregation to deal with rare events (“ Depending on the number of zero-count months, we adopted different mitigating strategies. […] if there were more zero counts per time period, we combined data into blocks of 2 months. ”) DISCUSSION 1. Summary of findings and comparison with other literature We examined discrepancies in the reporting of design and analysis methods between 44 ITS protocols and their corresponding reports of results. Across the items assessed, the percentage of studies with discrepancies ranged from 5% to 70% and the percentage of studies with potentially important discrepancies ranged from 2% to 45%. Our findings highlight problematic patterns in reporting of some items, particularly those related to the statistical analysis. First, characteristics of the time series were either frequently missing or discrepant between the protocol and report of results. Changes in these time series characteristics may affect the resulting estimates of the interruption’s impact. For example, having shorter series may not allow for accurate adjustment of cyclical patterns in the time series resulting in biased estimates of the impact of the interruption and of its standard error. Second, authors often did not specify the effect measures they would use to quantify the impact of the intervention. This is particularly problematic for ITS studies, where many effect measures can be calculated. Without pre-specifying which measures will be used, readers may not be able to determine when the investigators expected the interruption’s impact to occur (e.g., immediately or after a lag period). Absence of this pre-specification also creates opportunities for selective reporting of results and removes the opportunity for readers to detect such reporting. Third, the ITS analysis methods (e.g. OLS or Prais-Winsten GLS) were frequently not reported, reported with insufficient detail to determine if there was a discrepancy, or the methods differed between the protocol and report of results, often without justification. Other studies have similarly found that ITS analysis methods were frequently not reported ( 2 , 4 , 27 , 28 ). While a change to analysis methods might be driven by valid statistical considerations, failing to disclose these considerations makes it challenging for readers to discern whether the changes were warranted and how they might impact the results. Lastly, there was under-reporting of methods for handling autocorrelation and seasonality. This was particularly evident in protocols, where autocorrelation and seasonality were acknowledged in only 36% and 25% of protocols, respectively, compared with 50% and 34% in results reports (Additional File 7). These latter findings align with previous studies examining the reporting of statistical methods in results reports of ITS studies, where autocorrelation was acknowledged in 55% to 72% of the reports, and seasonality was acknowledged in 21% to 67% of the reports ( 6 – 8 , 33 , 35 ). Importantly, it is worth emphasising that the high percentages of discrepancies observed do not necessarily imply poor research conduct. Study design and analysis methods may change due to extenuating circumstances (e.g., issues with data collection, recruitment or implementation of intervention) ( 29 – 31 ) and in some cases, are necessary to improve analytical rigour. However, of concern is the high percentages of potentially important discrepancies that were not justified. As mentioned above, lack of explanation for changes in methods prevents readers from making judgement about how the changes would modify the results. As a result, readers were unable to assess the legitimacy of those changes, which may lead to unfair criticisms of the research ( 21 , 32 , 33 ). 3. Implications for practice We encourage investigators of ITS studies to publish publicly available protocols, with many pre-print servers and online registries available to support this. Many protocols in our sample were either focused on describing the intervention and the implementation processes, or were concurrently describing other study designs, leaving little room to adequately explain the ITS model and analysis methods. Given the complexity of the ITS analysis methods ( 5 – 7 ), we suggest that protocols for ITS studies should either be a standalone document, or be accompanied by a statistical analysis plan specifically for the ITS component. In developing the analysis plan, consideration might be given to specifying “forking paths” ( 34 , 35 ), where different analytical decisions will be made in response to anticipated challenges (e.g., if rare events are possible, describing what changes will be made to the choice of analysis methods). There is a need for reporting recommendations for protocols and study reports of ITS studies. Current reporting guidelines for non-randomised studies ( 36 , 37 ) do not provide tailored reporting recommendations for the features of interrupted time series design and analysis (e.g., methods for adjusting for autocorrelation and seasonality, description of the impact model). The CARITS (Complete and Accurate Reporting of Interrupted Time Series studies) guideline is being developed to fill this gap ( 38 ). Importantly, the guideline should recommend that authors specify and provide justification for any important changes to the original analysis plan. There is an established precedent for this in the CONSORT (CONsolidated Standards of Reporting Trials) guideline for randomized trials ( 13 ) and the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guideline for systematic reviews ( 39 ); however this recommendation is absent in the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for non-randomised studies ( 36 ). We observed that some changes had a cascading impact across the methods; for example, a change in time interval at which individual data were aggregated can lead to changes in the number of data points, which can impact the ability to detect autocorrelation and seasonality, as well as being able to accurately estimate the standard errors of the effect measures ( 5 ). This provides further justification as to the importance of documenting changes. 4. Strengths and limitations To our knowledge, this is the first study to investigate discrepancies in reporting of study designs and analysis methods in ITS studies. Similar research has been undertaken for randomised trials ( 21 , 40 , 41 ) and systematic reviews ( 42 ), but we are unaware of such investigations for non-randomised primary studies, potentially due to the fact that protocols are less commonly published for these types of studies. We used a validated search filter designed to locate ITS studies ( 25 ). We developed decision rules to facilitate consistent judgements about discrepancies. Our team included statisticians with specialist knowledge of the ITS design (JEM, EK, SLT) to judge whether the discrepancies could potentially affect the study findings. Our study is not without limitations. Our sample includes only ITS studies with published protocols, which is a very small fraction of all ITS studies ( 3 , 25 ). Publishing a protocol is associated with more complete and transparent reporting ( 43 , 44 ), and may reduce deviations from planned methods ( 45 ); therefore, our findings may provide optimistic estimates of the percentage of discrepancies from planned methods, as compared with ITS studies with no or unpublished protocols. CONCLUSION Study designs and analysis methods were often not, or insufficiently, reported in interrupted time series studies. Changes to original analysis plans were also prevalent and often unjustified, precluding readers from judging the legitimacy of the changes. Protocols for ITS studies should provide detailed information about the design and analysis methods. Deviations from planned methods should be transparently reported with clear justifications. Abbreviations CARITS Complete and Accurate Reporting of Interrupted Time Series studies CONSORT CONsolidated Standards of Reporting Trials GLS Generalised Least Squares ITS Interrupted Time Series OLS Ordinary Least Squares PRISMA Preferred Reporting Items for Systematic reviews and Meta-Analyses RCT Randomised Controlled Trial STROBE Strengthening the Reporting of Observational Studies in Epidemiology Declarations ACKNOWLEDGEMENT Ethics approval and consent to participate: Not applicable. This study was conducted using publicly available data and did not involve human participants, identifiable personal data, animal subjects, or interventions requiring ethical approval. Data related to the included studies was deidentified and anonymised using unique identification numbers. Consent to publication: Not applicable Availability of data and materials All datasets and analytical code can be found on the Open Science Framework (DOI: osf.io/9jqh3 and osf.io/phngv). Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by a Monash University’s Departmental Scholarship for P-YN’s Doctor of Philosophy (PhD) research program. MJP is supported by a NHMRC Investigator Grant (GNT2033917) and was supported by an Australian Research Council Discovery Early Career Researcher Award (DE200101618), the Research Support Package of Joanne E McKenzie’s NHMRC Investigator Grant (GNT2009612) and a Monash University Future Leader Postdoctoral Fellowship (FLPF23-1069865460) during the conduct of this research. SLT and EK were funded by the Research Support Package of Joanne E McKenzie’s NHMRC Investigator Grant (GNT2009612). JEM was supported by an NHMRC Investigator Grant (GNT2009612). The funders had not role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript. Transparency statement : The authors declare that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned have been reported in the Additional File. Patient and Public Involvement: Patients and the public were not involved in the design and conduct of this methodological research study. Contributorship Statement: All authors have reviewed and approved the final manuscript. All listed authors meet authorship criteria and that no other individuals meeting the criteria were omitted. JEM acted as the guarantor of this paper. PYN: conceptualisation, data curation, formal analysis, investigation, methodology, writing – original draft preparation EK: investigation, validation, writing – review and editing MJP, SLT, EK: investigation, validation, writing – review and editing, supervision JEM: conceptualisation, methodology, validation, formal analysis, writing – review and editing, supervision Acknowledgements: Not applicable References Lopez Bernal J, Soumerai S, Gasparrini A. A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol. 2018;103:82–91. Ewusie JE, Soobiah C, Blondal E, Beyene J, Thabane L, Hamid JS. Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review. J multidisciplinary Healthc. 2020;13:411–23. Hategeka C, Ruton H, Karamouzian M, Lynd LD, Law MR. Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review. BMJ global health. 2020;5(10):e003567. Hudson J, Fielding S, Ramsay CR. Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Med Res Methodol 2019 July 4;19(1):137. Lopez Bernal J, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46(1):348–55. Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Med Res Methodol. 2021 June;26(1):134. Turner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Med Res Methodol. 2021;21(1):1–18. Schaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):1–12. World Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013;310(20):2191–4. Jones G, Abbasi K. Trial protocols at the BMJ. BMJ. 2004;329(7479):1360. Lancet TE. of T. Protocol review at The Lancet: 1997–2015. The Lancet. 2015;386(10012):2456–7. Li T, Boutron I, Salman RAS, Cobo E, Flemyng E, Grimshaw JM, et al. Review and publication of protocol submissions to Trials – what have we learned in 10 years? Trials. 2016;18:34. Hopewell S, Chan AW, Collins GS, Hróbjartsson A, Moher D, Schulz KF, et al. CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials. BMJ. 2025;389:e081124. Haddaway NR, Macura B, Whaley P, Pullin AS. ROSES RepOrting standards for Systematic Evidence Syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environ Evid. 2018;7(1):7. Page MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. The BMJ [Internet]. 2021 Mar 29 [cited 2021 Sept 1];372. Available from: https://pubmed.ncbi.nlm.nih.gov/33781993/ Chan AW, Hróbjartsson A, Jørgensen KJ, Gøtzsche PC, Altman DG. Discrepancies in sample size calculations and data analyses reported in randomised trials: comparison of publications with protocols. BMJ. 2008;337:a2299. Chan AW, Hróbjartsson A, Haahr MT, Gøtzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA. 2004;291(20):2457–65. Chan AW, Krleza-Jerić K, Schmid I, Altman DG. Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research. CMAJ. 2004 Sept;28(7):735–40. Page MJ, McKenzie JE, Kirkham J, Dwan K, Kramer S, Green S, et al. Bias due to selective inclusion and reporting of outcomes and analyses in systematic reviews of randomised trials of healthcare interventions. Cochrane Database Syst Rev. 2014;2014(10):MR000035. Pieper D, Allers K, Mathes T, Hoffmann F, Klerings I, Rombey T, et al. Comparison of protocols and registry entries to published reports for systematic reviews. Cochrane Database Syst Rev. 2020;2020(2):MR000053. Coskinas X, Simes J, Schou M, Martin AJ. Changes to aspects of ongoing randomised controlled trials with fixed designs. Trials 2020 June 3;21(1):457. Korevaar DA, Ochodo EA, Bossuyt PMM, Hooft L. Publication and Reporting of Test Accuracy Studies Registered in ClinicalTrials.gov. Clin Chem. 2014;60(4):651–9. Rosati P, Porzsolt F, Ricciotti G, Testa G, Inglese R, Giustini F et al. Major discrepancies between what clinical trial registries record and paediatric randomised controlled trials publish. Trials 2016 Sept 23;17(1):430. Nguyen PY, Turner S, Korevaar E, Forbes A, Page MJ, McKenzie JE. Investigation of reporting bias in interrupted time series (ITS) studies: a study protocol. F1000Research [Internet]. 2024 Nov 4 [cited 2025 Apr 11]; Available from: https://f1000research.com/articles/13-153/v2 Nguyen PY, McKenzie JE, Turner SL, Page MJ, McDonald S. Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed. Res Synthesis Methods. 2024;15(4):627–40. World Bank. The World by Income and Region [Internet]. 2025 [cited 2025 Sept 24]. Available from: https://ddh-openapi.worldbank.org/resources/DR0095333/download Jandoc R, Burden AM, Mamdani M, Lévesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68(8):950–6. Turner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, et al. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020 June;1:122:1–11. Braat S, Lee KJ. Left in the dark: the importance of publicly available clinical trial protocols. Med J Aust [Internet]. 2022 Nov 21 [cited 2025 Aug 2];217(10). Available from: https://www.mja.com.au/journal/2022/217/10/left-dark-importance-publicly-available-clinical-trial-protocols#3 Chan AW, Tetzlaff JM, Gøtzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586. Little RJ, D’Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The Prevention and Treatment of Missing Data in Clinical Trials. N Engl J Med. 2012;367(14):1355–60. Wicherts JM, Veldkamp CLS, Augusteijn HEM, Bakker M, van Aert RCM, van Assen MALM. Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Front Psychol [Internet]. 2016 Nov 25 [cited 2025 Aug 12];7. Available from: https://www.frontiersin.org/journals/psychology/articles/ 10.3389/fpsyg.2016.01832/full Simmons JP, Nelson LD, Simonsohn U. False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychol Sci. 2011;22(11):1359–66. Habib N, Steyn PS, Boydell V, Cordero JP, Nguyen MH, Thwin SS, et al. The use of segmented regression for evaluation of an interrupted time series study involving complex intervention: the CaPSAI project experience. Health Serv Outcomes Res Methodol. 2021;21(2):188–205. Gelman A, Geurts HM. The statistical crisis in science: how is it relevant to clinical neuropsychology? The Clinical Neuropsychologist [Internet]. 2017 Oct 3 [cited 2025 Aug 12]; Available from: https://www.tandfonline.com/doi/abs/ 10.1080/13854046.2016.1277557 Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. Int J Surg. 2014;12(12):1500–24. Des Jarlais DC, Lyles C, Crepaz N, TREND Group. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. Am J Public Health. 2004;94(3):361–6. The EQUATOR, Network. CARITS – Complete and Accurate Reporting of Interrupted Time Series studies [Internet]. [cited 2025 Dec 10]. Available from: https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-observational-studies/#CARITS Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed) [Internet]. 2021 Mar 29 [cited 2022 Aug 23];372. Available from: https://pubmed.ncbi.nlm.nih.gov/33782057/ Dwan K, Altman DG, Clarke M, Gamble C, Higgins JPT, Sterne JAC, et al. Evidence for the selective reporting of analyses and discrepancies in clinical trials: a systematic review of cohort studies of clinical trials. PLoS Med. 2014 June;11(6):e1001666. Hartung D, Zarin DA, Guise JM, McDonagh M, Paynter R, Helfand M. Reporting Discrepancies between the ClinicalTrials.gov Results Database and Peer Reviewed Publications. Ann Intern Med. 2014;160(7):477–83. Siebert M, Caquelin L, Madera M, Acosta-Dighero R, Naudet F, Roqué M. Assessing the magnitude of changes from protocol to publication—a survey on Cochrane and non-Cochrane Systematic Reviews. PeerJ. 2023;11:e16016. Allers K, Hoffmann F, Mathes T, Pieper D. Systematic reviews with published protocols compared to those without: more effort, older search. J Clin Epidemiol. 2018;95:102–10. Innocenti T, Feller D, Giagio S, Salvioli S, Minnucci S, Brindisino F, et al. Adherence to the PRISMA statement and its association with risk of bias in systematic reviews published in rehabilitation journals: A meta-research study. Braz J Phys Ther. 2022;26(5):100450. Coskinas X, Simes RJ, Martin AJ. Changes to design and analysis elements of research plans during randomised controlled trials in Australia. Med J Aust [Internet]. 2022 Sept 26 [cited 2025 Aug 12];217(10). Available from: https://www.mja.com.au/journal/2022/217/10/changes-design-and-analysis-elements-research-plans-during-randomised Additional Declarations No competing interests reported. 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09:42:01","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71136,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/c1505d4a7932dbf7c64436a3.png"},{"id":101204359,"identity":"0ae0bc60-c60e-4e8d-a5e3-31df95eecf37","added_by":"auto","created_at":"2026-01-27 09:42:45","extension":"xml","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204456,"visible":true,"origin":"","legend":"","description":"","filename":"a793c86313674c99b2bb79795235a2391structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/5cd1ffc72ff2eca42d438b55.xml"},{"id":101019867,"identity":"6822c3c9-f358-44ed-a617-bfc2b1844a01","added_by":"auto","created_at":"2026-01-24 00:40:52","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":217332,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/837c7bb290126d24ff15eb50.html"},{"id":101204234,"identity":"cbaf192a-9291-47f8-8d0f-a09d84353de4","added_by":"auto","created_at":"2026-01-27 09:42:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":143016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart showing processes of literature search and screening\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"BIITSP3Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/d0276cbd8d68e367ed6f3848.png"},{"id":101019869,"identity":"c0acd284-8cff-4121-b8e2-0813a06c7334","added_by":"auto","created_at":"2026-01-24 00:40:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1136743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFrequency of discrepancies between protocols and results reports\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: A more detailed version of individual plots for each item is available in Additional File 9.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"BIITSP3Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/efc0a08f1d4390498664aa59.png"},{"id":101296722,"identity":"0ccfb690-5235-4b93-9d90-2903098d6f4b","added_by":"auto","created_at":"2026-01-28 09:19:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3682525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/19772760-f45a-4f2d-9f3f-2f1972c919f8.pdf"},{"id":101204151,"identity":"6bacbfa8-a1e1-4ac3-a300-373d360a8c98","added_by":"auto","created_at":"2026-01-27 09:41:47","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1143352,"visible":true,"origin":"","legend":"","description":"","filename":"BIITSP3AdditionalFile1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/c4cf65957f64b33f28cb21ff.pdf"},{"id":101019855,"identity":"b6af5ada-9d7a-4aab-8a6a-4bf4b819d898","added_by":"auto","created_at":"2026-01-24 00:40:51","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1117978,"visible":true,"origin":"","legend":"","description":"","filename":"BIITSP3AdditionalFiles210.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8612197/v1/46ff67e18ce02c37aaf79fcf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Discrepancies in reporting of study design and analysis methods between protocols and reports of interrupted time series (ITS) studies: a methodological study","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAn interrupted time series (ITS) study is a non-randomised design that can be characterised by the collection of data continuously over time, aggregation of this data using summary statistics (such as proportions) within regular time intervals (e.g., monthly), and separation of the resulting time series into segments by \u0026lsquo;interruption(s)\u0026rsquo;. The interruptions can be an exposure (e.g., disease outbreak) or an intervention (e.g., implementation of a national policy). The design can reduce the risk of selection and confounding biases caused by inter-group differences when using a control group (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). This makes the ITS design a robust alternative when randomisation may be unethical, infeasible or impractical, such as when evaluating interventions targeted at populations (\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany model structures and analysis methods may be used to analyse the ITS design. Commonly, a segmented regression model is fitted, which involves estimating the underlying time trend in the pre-interruption segment and extrapolating this into the post-interruption segment. This extrapolated trend provides a \u0026ldquo;counterfactual\u0026rdquo; for what would have occurred in the absence of the interruption. The impact of the interruption can then be estimated by comparing the counterfactual time trend with what was observed, using the estimated post-interruption time trend (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). From this comparison, a number of different effect measures can be calculated (e.g., immediate level change, change in slope). The model can be extended, and different analysis methods can be used, to handle issues associated with time series data, such as autocorrelation, seasonality, and time-varying confounders (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA protocol is a document that details the design and analysis methods for a research study. Protocols are an ethical principle for medical research involving human participants as stated in the World Medical Association\u0026rsquo;s Declaration of Helsinki (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Writing a protocol before conducting a research is highly encouraged in journal policies (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and reporting guidelines for randomised controlled trials (RCTs) and systematic reviews (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Protocols guide research teams in applying the methods, allow design improvements through peer review, and protect against biases arising from selective reporting of outcomes and inappropriate changes to the design and analysis methods. Perhaps inevitably, however, unanticipated circumstances arise that can lead to the need to deviate from the planned methods. Reporting of these deviations, along with why and when they occurred, allows users of the research to judge the potential for any risks of bias.\u003c/p\u003e \u003cp\u003eExamination of discrepancies in methods between protocols and results reports has been extensively investigated for randomised trials (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and systematic reviews (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), and have revealed substantial discrepancies in primary analysis methods and eligibility criteria (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). We are unaware of any such investigation of ITS studies. Given the importance of ITS studies in providing evidence for policy decisions, such an investigation is required.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOBJECTIVE\u003c/strong\u003e \u003cp\u003eTo examine discrepancies in the reporting of design and analysis methods between ITS protocols and their corresponding results reports.\u003c/p\u003e \u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study is part of a series of three studies investigating reporting biases among ITS studies, for which we have published a protocol (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) (Additional File 1). Here, we provide an overview of the methods and results for the third study; those of other studies are published separately. Deviations from the methods outlined in the protocol are available in Additional File 2.\u003c/p\u003e\n\u003ch3\u003e1. Creating a database of ITS studies\u003c/h3\u003e\n\u003cp\u003eWe searched for published, peer-reviewed protocols of ITS studies from 22 databases, using a highly sensitive search filter (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). For each protocol, we identified the \u0026ldquo;primary research question\u0026rdquo;, as stated by authors, or if not specified as primary, the first ITS research question listed in the aims or methods sections. We then searched the literature for the corresponding results reports that addressed the primary ITS research question from the protocol (see Additional Files S3 and S4 for details on literature search, screening and determining the primary research question). If there were multiple reports of results for a particular study, we included only the primary report, that is, the report that addressed the protocol\u0026rsquo;s primary ITS research question and most closely matched the study design in the protocol. ITS studies for which both a protocol and report of results were found constituted the sample for this study.\u003c/p\u003e\n\u003ch3\u003e2. Identifying and extracting data\u003c/h3\u003e\n\u003cp\u003eFrom each protocol and the report of results, we extracted information on study design and analysis methods across four areas:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eStudy design: the primary research question, eligibility criteria and data sources (labelled items A to C in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e3\u003c/span\u003e);\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of discrepancies between protocols and results reports\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiscrepancy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePotentially important discrepancy \u003csup\u003ea\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJustification not provided for discrepancy (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverview of study design\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(A) Primary research question\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13/44 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/44 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10/10 \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined \u003csup\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(B) Eligibility criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31/44 (70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19/44 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19/19 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(C) Data sources\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/44 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/44 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/10 (90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of the time series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(D) Overall length of the time series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/44 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/44 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/44 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13/17 (76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/44 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(E) Start and end dates of each segment in the time series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20/44 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17/44 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/44 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8/12 (67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(F) No. data points in each segment in the time series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/44 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27/44 (61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20/44 (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14/20 (70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/44 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eThe ITS model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(G) Start and end dates of each segment in the ITS model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/44 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15/44 (34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12/44 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/12 (58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(H) No. data points in each segment in the ITS model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12/44 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22/44 (50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17/44 (39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13/17 (76%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(I) Time interval(s) at which outcome data was aggregated\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/44 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8/44 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8/44 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7/8 (88%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(J) How the interruption was modelled\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/44 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/3 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(K) Which segments were compared to address the primary research question\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6/44 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/44 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/3 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(L) Types of effect measures reported\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13/44 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/44 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11/44 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11/11 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatistical analysis methods\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(M) ITS analysis method(s)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/44 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14/44 (32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10/44 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9/10 (90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10/44 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(N) Decision rule on whether to adjust for autocorrelation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34/44 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4/44 (9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/2 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(O) Method(s) of testing for autocorrelation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30/36\u003csup\u003ed\u003c/sup\u003e (83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/36 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/36 (8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2/3 (67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/36 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(P) Method(s) of adjusting for autocorrelation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34/44 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/44 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/1 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/44 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(Q) Method(s) of testing \u0026amp; adjusting for seasonality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36/44 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5/44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3/44 (7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3/3 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(R) Method(s) of testing \u0026amp; adjusting for non-stationarity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41/44 (93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0/44 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(S) Presence and type of control series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2/44 (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7/44 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5/44 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4/5 (80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1/44 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e(T) Method(s) of comparing intervention and control series\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing information in protocol and/or results report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9/20\u003csup\u003ee\u003c/sup\u003e (45%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny discrepancy between results report and protocol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/20 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2/20 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/2 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot be determined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3/20 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes: Three types of discrepancies were recorded\u003c/em\u003e: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) \u003cem\u003eresults report details did not match protocol;\u003c/em\u003e (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) \u003cem\u003eresults report had fewer details than protocol;\u003c/em\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) \u003cem\u003eresults report had more details than protocol. See Additional File 10 for a breakdown of these three types of discrepancies.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eDiscrepancy had potential to significantly impact the results. See Additional File 6 for examples.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eDenominator is the number of studies with important discrepancy between the protocol and the results report.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e \u003cem\u003e\u0026ldquo;Cannot be determined\u0026rdquo; is applicable to studies that had some information about the item reported in both the protocol and the results report, but the information was either too vague or insufficient to determine whether there was a discrepancy, or what the type of discrepancy was.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eFor \u0026ldquo;Method(s) of testing for autocorrelation\u0026rdquo;, the denominator only includes studies where the authors said they might test for presence of autocorrelation.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sup\u003e \u003cem\u003eFor \u0026ldquo;Method(s) of comparing intervention and control series\u0026rdquo;, the denominator only includes studies where there was a control series.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eAbbreviations: ITS: interrupted time series\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCharacteristics of the time series: overall length in terms of number of data points, length of each segment, and start and end date of each segment (items D to F);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eITS model: length of each segment and start and end date of each segment based on model parameters (items G and H), the time intervals at which data were aggregated, how the interruption was modelled, the segments to be compared, and the types of effect measures generated (items I to L);\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStatistical methods: ITS analysis methods (e.g., ordinary least squares [OLS], Prais-Winsten generalised least squares [GLS]), item M), methods to detect and handle autocorrelation, seasonality and non-stationarity (items N-R), presence of control series and methods to compare intervention with control series (items S to T).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eTo pilot the data extraction form (Additional File 5), all five authors (PYN/JEM/SLT/EK/MJP) independently extracted data from the same five studies to ensure a shared understanding of the process. Data from the remaining studies were extracted by two reviewers (PYN and one of JEM/SLT/EK/MJP). Any difference in extracted data was resolved via discussion between the reviewers or at regular team meetings.\u003c/p\u003e\n\u003ch3\u003e3. Assessing discrepancies in methods between protocols and reports of results\u003c/h3\u003e\n\u003cp\u003eFor each item, two reviewers assessed whether there was any discrepancy between what was reported in the protocol and the report of results. Each item was classified into one of the following categories:\u003c/p\u003e \u003cp\u003e\u003cem\u003eDiscrepancy categories:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(1) \u0026nbsp;Report of results had fewer details than protocol: the protocol had some information about the item that was not mentioned in the report of results, but the provided details about the item were aligned.\u003c/p\u003e\n\u003cp\u003e(2) \u0026nbsp;Report of results had more details than protocol: the report of results had some information about the item that was not mentioned in the protocol, but the provided details about the item were aligned.\u003c/p\u003e\n\u003cp\u003e(3)\u0026nbsp;\u0026nbsp;Report of results\u0026rsquo; details did not match the protocol\u0026rsquo;s: some information about the item was provided in both documents but the information was not aligned.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNo discrepancy category:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(4)\u0026nbsp;\u0026nbsp;Report of results\u0026rsquo; details matched those from the protocol: both documents provided a similar level of detail about the item and all the details were consistent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNot assessed for discrepancies:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e(5)\u0026nbsp;\u0026nbsp;Cannot be determined: some information was provided about the item in both the protocol and report of results, but there was insufficient detail to enable a judgement of (1) to (4).\u003c/p\u003e\n\u003cp\u003e(6) \u0026nbsp;Missing information in protocol, report of results, or both: no information was provided at all about the item in either of the documents, or both.\u003c/p\u003e\n\u003cp\u003eCategories (1)-(3) were considered discrepancies while category (4) was not considered a discrepancy. Categories (5) and (6), were not assessed for discrepancy, due to insufficient information in one or both of the documents (see Table 1 for examples).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssessing discrepancies in reporting of item D \u0026ndash; Overall length of the time series| Examples for each type of discrepancy\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE.g.,\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAs reported in protocol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAs reported in results report\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRationale for our discrepancy assessment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAssessment of discrepancy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo information available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA figure shows a plot of the ITS with 24 data points.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot assessed for discrepancy\u003c/strong\u003e: Missing information in protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe equation of the ITS model describes: \u0026ldquo;\u003cem\u003et\u003c/em\u003e is the time in months from the start of the observation period, ranging from 1 to 22 months\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo information available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom the protocol, it could be inferred that there were 22 data points (22 months of monthly data).\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot assessed for discrepancy\u003c/strong\u003e: Missing information in results report\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo information available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;We will use ITS analyses to assess changes in healthcare attendances from 2013 to 2016\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom the results report, the time interval for data aggregation was not reported, so we could not deduce the number of data points between 2013 and 2016.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot assessed for discrepancy\u003c/strong\u003e: Missing information in both protocol and results report\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026quot;The ITS will include routinely collected data spanning July 2007 to January 2012, providing 45 pre- and 10 post-intervention data points.\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA figure shows a plot of the ITS with 55 data points.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrom the protocol, the total number of data points summed to 55, which is consistent with the number of data points shown in the figure.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo discrepancy\u003c/strong\u003e: Results report details matched with the protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA figure shows individual plots of ITS for three intervention sites, showing 30, 32 and 30 data points, respectively.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026quot;The ITS analysis had 12 data points in the pre-implementation period and 18 data points in the post-implementation periods\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe results report did not provide as much detail as the protocol in providing the number of data points for all sites.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrepancy\u003c/strong\u003e: Results report had fewer details than protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Each data set will cover at least 2 years of data on monthly population-based vaccination rates on either side of vaccine introduction\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;We analysed monthly population-based vaccination rates using 27 months of data before and 45 months of data after the introduction of the vaccine\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe protocol suggests there will be \u003cem\u003eat least\u003c/em\u003e 48 data points, which is consistent with what was reported in the results report (72 data points) but not an exact match.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrepancy\u003c/strong\u003e: Results report had more details than protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;The ITS consists of two time periods: 12 months of preintervention (3 data points) and 12 months of postintervention (3 data points).\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026quot;The data points for the time series data represent the proportion of patients hospitalised per two months (i.e., six data points before and six data points after the intervention each consisting of at least 30 patients)\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe numbers of data points were different due to different time intervals of aggregation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrepancy\u003c/strong\u003e: Results report details did not match the protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;The study will be conducted in three phases: pre-implementation (January to March 2023); implementation (April 2023 to March 2024 planned); and maintenance (April 2024 to March 2025).\u0026rdquo;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Pre-implementation and implementation activities were carried out between January 2023 and March 2024, followed by a maintenance period up to June 2025\u0026rdquo;.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIn both the protocol and the results report, the time intervals of the data were not reported, so the exact number of data points could not be deduced.\u003c/p\u003e\n \u003cp\u003eHowever, the end dates of the maintenance period were clearly different, so it is highly likely that the total number of data points would have differed as well.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiscrepancy\u003c/strong\u003e: Results report details did not match the protocol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ldquo;Monthly data on contraceptive use is recorded from six months prior to baseline and every month until end-line (at 24 months after baseline).\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eText: \u0026ldquo;31 months of data were collected from September 2017 to March 2020\u0026rdquo;.\u003c/p\u003e\n \u003cp\u003eFigure shows a plot of the ITS commencing in August 2017 and ending in January 2020.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThere was information in the protocol to determine the number of data points (30 months x monthly data\u0026thinsp;=\u0026thinsp;30 data points).\u003c/p\u003e\n \u003cp\u003eHowever, in the results report, the number of data points could be either 31 (based on text) or 30 (based on figure).\u003c/p\u003e\n \u003cp\u003eHence, it is impossible for the reviewers to decide whether there was a discrepancy or not.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot assessed for discrepancy\u003c/strong\u003e: Cannot be determined\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes: (a) The text within quotation marks is text extracted from the included ITS protocols and results reports (modified for confidentiality). All other text is the reviewer\u0026rsquo;s remarks/observations. (b) We considered all information irrespective of where it was reported (e.g., figures, equations, tables, text). If there were discrepancies between different sources of information within a protocol or within a results report, two independent reviewers discussed to select the most reliable information, giving priority to the information that was supported by more than one sources. If it was impossible to decide based on the available information, we did not assess for discrepancy (see example 9).\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cem\u003eAbbreviations: ITS: interrupted time series\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWe judged the potential importance of each discrepancy (i.e., whether it would potentially impact the results of the analysis or the conclusion of the study, see Additional File 6 for examples). For each potentially important discrepancy, we extracted verbatim any rationale for the deviation provided by the authors.\u003c/p\u003e\n\u003cp\u003eAll assessments were undertaken independently by pairs of reviewers (PYN/MJP or EK/SLT). Differences in assessment were discussed by the pairs to reach consensus. Finally, PYN checked that the decision rules were applied consistently across all studies.\u003c/p\u003e\n\u003ch3\u003e4. Statistical analysis\u003c/h3\u003e\n\u003cp\u003eWe calculated the frequency of reporting for each item of the study design and analysis methods. For each item, we calculated the percentage of studies with: any discrepancy [categories (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e)-(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e)]; any potentially important discrepancy; and any potentially important discrepancy with a justification. We categorised the reported justifications for the discrepancies.\u003c/p\u003e"},{"header":"RESULTS","content":"\n\u003ch3\u003e1. Overview of the search process\u003c/h3\u003e\n\u003cp\u003eWe screened 4,590 abstracts and the full text of 148 ITS study protocols. After excluding protocols that did not meet inclusion criteria and ongoing studies, 120 ITS study protocols were selected. A search for corresponding reports of results identified 467 potentially eligible full texts, from which 44 met our inclusion criteria. Of the 120 ITS study protocols, 76/120 (63%) did not have a matching report of results, while 44/120 (37%) had at least one matched report of results. These 44 pairs of protocols and corresponding reports of results constituted the sample for this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2. Characteristics of included studies\u003c/h3\u003e\n\u003cp\u003eThe ITS protocols were published between 2010 and 2022 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most studies (33/44, 75%) outlined methods for other study designs (e.g., qualitative analysis) in addition to the ITS study analysis. Half of the studies (22/44, 50%) were registered. Most studies (37/44, 84%) were supported by non-industry funding and most (36/44, 82%) were conducted in a high-income country (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In the results reports of these studies, the median number of data points in the time series was 36 (IQR 24 to 70) and aggregation of outcome data to monthly intervals was most common (26/44, 59%). (see Additional File 7 for further characteristics of the design and analysis methods).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of included studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. ITS studies (%)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFocus of the study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnly ITS study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITS and other study designs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-industry funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry funding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy registration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNature of the interruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExposure (natural events)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (100%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice change in a clinical setting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (57%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth system interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy \u0026amp; regulatory changes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial \u0026amp; economic interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental interventions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel at which the intervention was implemented\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnit-based or institutional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultinational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel at which the intervention was evaluated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnit-based or institutional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (23%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultinational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry where study was conducted\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh-income countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (82%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper middle-income countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLower middle-income countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-income countries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTiming of data collection relative to the protocol\u0026rsquo;s submission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetrospective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (39%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProspective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (61%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNotes: For details of how we defined the options for each characteristic, refer to Additional File 5 (Data extraction form). Abbreviation: ITS: interrupted time series\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003cem\u003eBased on World Blank Group\u0026rsquo;s FY25 income classification. Total of percentages may exceed 100% as multiple response options could apply.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003e3. Completeness of reporting (Table 3 \u0026 Fig. 2)\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Items related to study design\u003c/h2\u003e \u003cp\u003eThe primary research question, eligibility criteria and data sources (items A-C) were commonly reported, with only 2/44 studies (5%) having missing information about eligibility criteria and 2/44 studies (5%) not reporting sufficient detail about the data sources for discrepancy assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Items related to characteristics of time series\u003c/h2\u003e \u003cp\u003eThe overall length of the time series (item D) and number of data points of each segment (item F) in the time series were commonly reported, with a fifth of the studies missing information about these items (9/44 studies [20%] for item D and 7/44 [16%] for item F). However, almost half of the studies (20/44 [45%]) had missing information on the start or end date of each time segment (item E). For these items, the number of studies that did not report sufficient information for discrepancy assessment was less than five.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Items related to the ITS model\u003c/h2\u003e \u003cp\u003eInformation was missing on start and end date of the modelled time series (item G) in half of the studies (22/44 [50%]), and on number of data points of each modelled segment (item H) in a third of studies (12/44 [27%]).\u003c/p\u003e \u003cp\u003eA smaller percentage of studies had missing information on the time intervals for outcome aggregation (item I, 6/44 studies [14%]), how the interruption was modelled (item J, 7/44 [16%]), and the segments to be compared (item K, 6/44 [14%]). Almost a third of studies (13/44 studies [30%]) had missing information about the type of effect measure(s) (item L) to be reported, and in a further 7%, the effect measures could not be determined.\u003c/p\u003e \u003cp\u003eFor each item, the number of studies that did not report the information sufficient for discrepancy assessment was five or fewer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Items related to statistical methods\u003c/h2\u003e \u003cp\u003eThe ITS analysis methods (item M) were not commonly reported, with a third of the studies (14/44 [32%]) not reporting the methods and another quarter of the studies (10/44 [23%]) providing insufficient detail to allow for a discrepancy assessment.\u003c/p\u003e \u003cp\u003eIn a large percentage of studies, the authors did not report on whether they would use a decision rule to adjust for autocorrelation (item N, 32/44 [77%]) or the method of adjusting for autocorrelation (item P, 34/44 [77%]). Among the studies where the authors stated they would test for the presence of autocorrelation, the method of testing (item O) was not reported in 30/36 studies (83%). Similarly, information was frequently missing on the methods for adjusting for seasonality (item Q, 36/44 studies [82%]) and stationarity (item R) (41/44 [93%]).\u003c/p\u003e \u003cp\u003eIn most studies, the presence (or not) of a control series was commonly reported (item S), with only 2/44 studies (5%) not reporting this information. However, among 20 studies with control series, 9 (45%) reported no information on the methods for comparing intervention series with control series (item T), and 3 (15%) had insufficient information to assess discrepancies for this item.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e4. Discrepancies in reporting (Table 3 \u0026 Fig. 2)\u003c/h3\u003e\n\u003cp\u003eIn this section, we report the frequency of discrepancies between protocols and results reports. Specific details of the design and analysis methods reported in the protocols and the results reports are available in Additional File 7.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Items related to study design\u003c/h2\u003e \u003cp\u003eA quarter of the studies (10/44, 23%) had potentially important discrepancies in the primary research question (item A) or data sources (item C), with all or most of these discrepancies not justified (100% and 90%, respectively). Similarly, 19/44 studies (43%) had potentially important discrepancies in eligibility criteria (item B), all of which were unjustified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Items related to characteristics of time series\u003c/h2\u003e \u003cp\u003eA high percentage of studies had potentially important discrepancies in reporting the overall length of the time series (item D, 17/44 studies [39%]), start and end dates of each segment (item E, 12/44 [27%]) and the number of data points in each segment in the time series (item F, 20/44 [45%]). For each item, greater than two-thirds of the discrepancies were not justified (13/17 studies [76%] for item D, 8/12 [67%] for item E, and 14/20 [70%] for item F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Items related to the ITS model\u003c/h2\u003e \u003cp\u003eThe patterns of potentially important and unjustified discrepancies in the length (item H), and start and end dates of each segment (item G) used in the ITS model mirrored what was observed in the design of the time series.\u003c/p\u003e \u003cp\u003eA smaller percentage of studies had potentially important discrepancies in the time intervals for outcome aggregation (item I, 8/44 studies [18%]), how the interruption was modelled (item J, 3/44 [7%]), and the segments to be compared (item K, 3/44 [7%])). However, most or all of these discrepancies were unjustified (7/8 studies [88%] for item I, 3/3 [100%] for item J, and 3/3 [100%] for item K).\u003c/p\u003e \u003cp\u003eA quarter of studies had potentially important discrepancies in the types of effect measures reported (item L, 11/44 studies [25%]), all of which were unjustified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Items related to statistical methods\u003c/h2\u003e \u003cp\u003eA quarter of studies (10/44 [23%]) had potentially important discrepancies in ITS analysis methods (item M), with nearly all being unjustified (9/10 studies [90%]).\u003c/p\u003e \u003cp\u003eAutocorrelation, seasonality and non-stationarity were rarely mentioned (items N-R), resulting in very few studies where we could compare and assess for discrepancies in the methods to handle them. However, most or all of the potentially important discrepancies (67% to 100%) were not justified.\u003c/p\u003e \u003cp\u003eA small percentage of studies had potentially important discrepancies in the presence (or absence) of control series (5/44 [11%], item S). Among studies with control series, 2/20 studies [10%] had potentially important discrepancies in the methods of comparing intervention series with control series (item T), with one discrepancy unjustified.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e5. Justifications for discrepancies\u003c/h3\u003e\n\u003cp\u003eIn 10/44 studies (23%), the authors provided a justification for at least one potentially important discrepancy (Additional File 8). Some justifications reflected factors outside of the investigators control such as impacts of COVID-10 restrictions, lack of funding, or inability to obtain data from control sites. Other justifications reflected changes made by the investigators such as changes to statistical methods to deal with unforeseen problematic data. For example, in one study, the authors changed the time intervals of outcome aggregation to deal with rare events (\u0026ldquo;\u003cem\u003eDepending on the number of zero-count months, we adopted different mitigating strategies. [\u0026hellip;] if there were more zero counts per time period, we combined data into blocks of 2 months.\u003c/em\u003e\u0026rdquo;)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\n\u003ch3\u003e1. Summary of findings and comparison with other literature\u003c/h3\u003e\n\u003cp\u003eWe examined discrepancies in the reporting of design and analysis methods between 44 ITS protocols and their corresponding reports of results. Across the items assessed, the percentage of studies with discrepancies ranged from 5% to 70% and the percentage of studies with potentially important discrepancies ranged from 2% to 45%.\u003c/p\u003e \u003cp\u003eOur findings highlight problematic patterns in reporting of some items, particularly those related to the statistical analysis. First, characteristics of the time series were either frequently missing or discrepant between the protocol and report of results. Changes in these time series characteristics may affect the resulting estimates of the interruption\u0026rsquo;s impact. For example, having shorter series may not allow for accurate adjustment of cyclical patterns in the time series resulting in biased estimates of the impact of the interruption and of its standard error.\u003c/p\u003e \u003cp\u003eSecond, authors often did not specify the effect measures they would use to quantify the impact of the intervention. This is particularly problematic for ITS studies, where many effect measures can be calculated. Without pre-specifying which measures will be used, readers may not be able to determine when the investigators expected the interruption\u0026rsquo;s impact to occur (e.g., immediately or after a lag period). Absence of this pre-specification also creates opportunities for selective reporting of results and removes the opportunity for readers to detect such reporting.\u003c/p\u003e \u003cp\u003eThird, the ITS analysis methods (e.g. OLS or Prais-Winsten GLS) were frequently not reported, reported with insufficient detail to determine if there was a discrepancy, or the methods differed between the protocol and report of results, often without justification. Other studies have similarly found that ITS analysis methods were frequently not reported (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While a change to analysis methods might be driven by valid statistical considerations, failing to disclose these considerations makes it challenging for readers to discern whether the changes were warranted and how they might impact the results.\u003c/p\u003e \u003cp\u003eLastly, there was under-reporting of methods for handling autocorrelation and seasonality. This was particularly evident in protocols, where autocorrelation and seasonality were acknowledged in only 36% and 25% of protocols, respectively, compared with 50% and 34% in results reports (Additional File 7). These latter findings align with previous studies examining the reporting of statistical methods in results reports of ITS studies, where autocorrelation was acknowledged in 55% to 72% of the reports, and seasonality was acknowledged in 21% to 67% of the reports (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, it is worth emphasising that the high percentages of discrepancies observed do not necessarily imply poor research conduct. Study design and analysis methods may change due to extenuating circumstances (e.g., issues with data collection, recruitment or implementation of intervention) (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and in some cases, are necessary to improve analytical rigour. However, of concern is the high percentages of potentially important discrepancies that were not justified. As mentioned above, lack of explanation for changes in methods prevents readers from making judgement about how the changes would modify the results. As a result, readers were unable to assess the legitimacy of those changes, which may lead to unfair criticisms of the research (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e3. Implications for practice\u003c/h3\u003e\n\u003cp\u003eWe encourage investigators of ITS studies to publish publicly available protocols, with many pre-print servers and online registries available to support this. Many protocols in our sample were either focused on describing the intervention and the implementation processes, or were concurrently describing other study designs, leaving little room to adequately explain the ITS model and analysis methods. Given the complexity of the ITS analysis methods (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), we suggest that protocols for ITS studies should either be a standalone document, or be accompanied by a statistical analysis plan specifically for the ITS component. In developing the analysis plan, consideration might be given to specifying \u0026ldquo;forking paths\u0026rdquo; (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), where different analytical decisions will be made in response to anticipated challenges (e.g., if rare events are possible, describing what changes will be made to the choice of analysis methods).\u003c/p\u003e \u003cp\u003eThere is a need for reporting recommendations for protocols and study reports of ITS studies. Current reporting guidelines for non-randomised studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) do not provide tailored reporting recommendations for the features of interrupted time series design and analysis (e.g., methods for adjusting for autocorrelation and seasonality, description of the impact model). The CARITS (Complete and Accurate Reporting of Interrupted Time Series studies) guideline is being developed to fill this gap (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Importantly, the guideline should recommend that authors specify and provide justification for any important changes to the original analysis plan. There is an established precedent for this in the CONSORT (CONsolidated Standards of Reporting Trials) guideline for randomized trials (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guideline for systematic reviews (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e); however this recommendation is absent in the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for non-randomised studies (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). We observed that some changes had a cascading impact across the methods; for example, a change in time interval at which individual data were aggregated can lead to changes in the number of data points, which can impact the ability to detect autocorrelation and seasonality, as well as being able to accurately estimate the standard errors of the effect measures (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). This provides further justification as to the importance of documenting changes.\u003c/p\u003e\n\u003ch3\u003e4. Strengths and limitations\u003c/h3\u003e\n\u003cp\u003eTo our knowledge, this is the first study to investigate discrepancies in reporting of study designs and analysis methods in ITS studies. Similar research has been undertaken for randomised trials (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) and systematic reviews (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), but we are unaware of such investigations for non-randomised primary studies, potentially due to the fact that protocols are less commonly published for these types of studies. We used a validated search filter designed to locate ITS studies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). We developed decision rules to facilitate consistent judgements about discrepancies. Our team included statisticians with specialist knowledge of the ITS design (JEM, EK, SLT) to judge whether the discrepancies could potentially affect the study findings.\u003c/p\u003e \u003cp\u003eOur study is not without limitations. Our sample includes only ITS studies with published protocols, which is a very small fraction of all ITS studies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Publishing a protocol is associated with more complete and transparent reporting (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), and may reduce deviations from planned methods (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e); therefore, our findings may provide optimistic estimates of the percentage of discrepancies from planned methods, as compared with ITS studies with no or unpublished protocols.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eStudy designs and analysis methods were often not, or insufficiently, reported in interrupted time series studies. Changes to original analysis plans were also prevalent and often unjustified, precluding readers from judging the legitimacy of the changes. Protocols for ITS studies should provide detailed information about the design and analysis methods. Deviations from planned methods should be transparently reported with clear justifications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCARITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComplete and Accurate Reporting of Interrupted Time Series studies\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCONSORT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCONsolidated Standards of Reporting Trials\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralised Least Squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterrupted Time Series\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrdinary Least Squares\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic reviews and Meta-Analyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomised Controlled Trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study was conducted using publicly available data and did not involve human participants, identifiable personal data, animal subjects, or interventions requiring ethical approval. Data related to the included studies was deidentified and anonymised using unique identification numbers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets and analytical code can be found on the Open Science Framework (DOI: osf.io/9jqh3 and osf.io/phngv).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by a Monash University\u0026rsquo;s Departmental Scholarship for P-YN\u0026rsquo;s Doctor of Philosophy (PhD) research program. MJP is supported by a NHMRC Investigator Grant (GNT2033917) and was supported by an Australian Research Council Discovery Early Career Researcher Award (DE200101618), the Research Support Package of Joanne E McKenzie\u0026rsquo;s NHMRC Investigator Grant (GNT2009612) and a Monash University Future Leader Postdoctoral Fellowship (FLPF23-1069865460) during the conduct of this research. SLT and EK were funded by the Research Support Package of Joanne E McKenzie\u0026rsquo;s NHMRC Investigator Grant (GNT2009612). JEM was supported by an NHMRC Investigator Grant (GNT2009612). The funders had not role in the conceptualization, design, data collection, analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransparency statement\u003c/strong\u003e: The authors declare that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as originally planned have been reported in the Additional File.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient and Public Involvement: Patients and the public were not involved in the design and conduct of this methodological research study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributorship Statement: All authors have reviewed and approved the final manuscript. All listed authors meet authorship criteria and that no other individuals meeting the criteria were omitted. JEM acted as the guarantor of this paper.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePYN: conceptualisation, data curation, formal analysis, investigation, methodology, writing \u0026ndash; original draft preparation\u003c/p\u003e\n\u003cp\u003eEK: investigation, validation, writing \u0026ndash; review and editing\u003c/p\u003e\n\u003cp\u003eMJP, SLT, EK: investigation, validation, writing \u0026ndash; review and editing, supervision\u003c/p\u003e\n\u003cp\u003eJEM: conceptualisation, methodology, validation, formal analysis, writing \u0026ndash; review and editing, supervision\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements: Not applicable\u003c/strong\u003e\u003cstrong\u003e\u003cbr\u003e \u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLopez Bernal J, Soumerai S, Gasparrini A. A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol. 2018;103:82\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEwusie JE, Soobiah C, Blondal E, Beyene J, Thabane L, Hamid JS. Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review. J multidisciplinary Healthc. 2020;13:411\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHategeka C, Ruton H, Karamouzian M, Lynd LD, Law MR. Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review. BMJ global health. 2020;5(10):e003567.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHudson J, Fielding S, Ramsay CR. Methodology and reporting characteristics of studies using interrupted time series design in healthcare. BMC Med Res Methodol 2019 July 4;19(1):137.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez Bernal J, Cummins S, Gasparrini A. Interrupted time series regression for the evaluation of public health interventions: a tutorial. Int J Epidemiol. 2017;46(1):348\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, McKenzie JE. Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series. BMC Med Res Methodol. 2021 June;26(1):134.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner SL, Forbes AB, Karahalios A, Taljaard M, McKenzie JE. Evaluation of statistical methods used in the analysis of interrupted time series studies: a simulation study. BMC Med Res Methodol. 2021;21(1):1\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Medical Association. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013;310(20):2191\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones G, Abbasi K. Trial protocols at the BMJ. BMJ. 2004;329(7479):1360.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLancet TE. of T. Protocol review at The Lancet: 1997\u0026ndash;2015. The Lancet. 2015;386(10012):2456\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi T, Boutron I, Salman RAS, Cobo E, Flemyng E, Grimshaw JM, et al. Review and publication of protocol submissions to Trials \u0026ndash; what have we learned in 10 years? Trials. 2016;18:34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHopewell S, Chan AW, Collins GS, Hr\u0026oacute;bjartsson A, Moher D, Schulz KF, et al. CONSORT 2025 explanation and elaboration: updated guideline for reporting randomised trials. BMJ. 2025;389:e081124.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaddaway NR, Macura B, Whaley P, Pullin AS. ROSES RepOrting standards for Systematic Evidence Syntheses: pro forma, flow-diagram and descriptive summary of the plan and conduct of environmental systematic reviews and systematic maps. Environ Evid. 2018;7(1):7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, Moher D, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews. The BMJ [Internet]. 2021 Mar 29 [cited 2021 Sept 1];372. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33781993/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33781993/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan AW, Hr\u0026oacute;bjartsson A, J\u0026oslash;rgensen KJ, G\u0026oslash;tzsche PC, Altman DG. Discrepancies in sample size calculations and data analyses reported in randomised trials: comparison of publications with protocols. BMJ. 2008;337:a2299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan AW, Hr\u0026oacute;bjartsson A, Haahr MT, G\u0026oslash;tzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: comparison of protocols to published articles. JAMA. 2004;291(20):2457\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan AW, Krleza-Jerić K, Schmid I, Altman DG. Outcome reporting bias in randomized trials funded by the Canadian Institutes of Health Research. CMAJ. 2004 Sept;28(7):735\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Kirkham J, Dwan K, Kramer S, Green S, et al. Bias due to selective inclusion and reporting of outcomes and analyses in systematic reviews of randomised trials of healthcare interventions. Cochrane Database Syst Rev. 2014;2014(10):MR000035.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePieper D, Allers K, Mathes T, Hoffmann F, Klerings I, Rombey T, et al. Comparison of protocols and registry entries to published reports for systematic reviews. Cochrane Database Syst Rev. 2020;2020(2):MR000053.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoskinas X, Simes J, Schou M, Martin AJ. Changes to aspects of ongoing randomised controlled trials with fixed designs. Trials 2020 June 3;21(1):457.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorevaar DA, Ochodo EA, Bossuyt PMM, Hooft L. Publication and Reporting of Test Accuracy Studies Registered in ClinicalTrials.gov. Clin Chem. 2014;60(4):651\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosati P, Porzsolt F, Ricciotti G, Testa G, Inglese R, Giustini F et al. Major discrepancies between what clinical trial registries record and paediatric randomised controlled trials publish. Trials 2016 Sept 23;17(1):430.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen PY, Turner S, Korevaar E, Forbes A, Page MJ, McKenzie JE. Investigation of reporting bias in interrupted time series (ITS) studies: a study protocol. F1000Research [Internet]. 2024 Nov 4 [cited 2025 Apr 11]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://f1000research.com/articles/13-153/v2\u003c/span\u003e\u003cspan address=\"https://f1000research.com/articles/13-153/v2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen PY, McKenzie JE, Turner SL, Page MJ, McDonald S. Development of a search filter to retrieve reports of interrupted time series studies from MEDLINE and PubMed. Res Synthesis Methods. 2024;15(4):627\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. The World by Income and Region [Internet]. 2025 [cited 2025 Sept 24]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ddh-openapi.worldbank.org/resources/DR0095333/download\u003c/span\u003e\u003cspan address=\"https://ddh-openapi.worldbank.org/resources/DR0095333/download\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJandoc R, Burden AM, Mamdani M, L\u0026eacute;vesque LE, Cadarette SM. Interrupted time series analysis in drug utilization research is increasing: systematic review and recommendations. J Clin Epidemiol. 2015;68(8):950\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner SL, Karahalios A, Forbes AB, Taljaard M, Grimshaw JM, Cheng AC, et al. Design characteristics and statistical methods used in interrupted time series studies evaluating public health interventions: a review. J Clin Epidemiol. 2020 June;1:122:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraat S, Lee KJ. Left in the dark: the importance of publicly available clinical trial protocols. Med J Aust [Internet]. 2022 Nov 21 [cited 2025 Aug 2];217(10). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mja.com.au/journal/2022/217/10/left-dark-importance-publicly-available-clinical-trial-protocols#3\u003c/span\u003e\u003cspan address=\"https://www.mja.com.au/journal/2022/217/10/left-dark-importance-publicly-available-clinical-trial-protocols#3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChan AW, Tetzlaff JM, G\u0026oslash;tzsche PC, Altman DG, Mann H, Berlin JA, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittle RJ, D\u0026rsquo;Agostino R, Cohen ML, Dickersin K, Emerson SS, Farrar JT, et al. The Prevention and Treatment of Missing Data in Clinical Trials. N Engl J Med. 2012;367(14):1355\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWicherts JM, Veldkamp CLS, Augusteijn HEM, Bakker M, van Aert RCM, van Assen MALM. Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking. Front Psychol [Internet]. 2016 Nov 25 [cited 2025 Aug 12];7. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/psychology/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/psychology/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fpsyg.2016.01832/full\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2016.01832/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimmons JP, Nelson LD, Simonsohn U. False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychol Sci. 2011;22(11):1359\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabib N, Steyn PS, Boydell V, Cordero JP, Nguyen MH, Thwin SS, et al. The use of segmented regression for evaluation of an interrupted time series study involving complex intervention: the CaPSAI project experience. Health Serv Outcomes Res Methodol. 2021;21(2):188\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGelman A, Geurts HM. The statistical crisis in science: how is it relevant to clinical neuropsychology? The Clinical Neuropsychologist [Internet]. 2017 Oct 3 [cited 2025 Aug 12]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.tandfonline.com/doi/abs/\u003c/span\u003e\u003cspan address=\"https://www.tandfonline.com/doi/abs/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/13854046.2016.1277557\u003c/span\u003e\u003cspan address=\"10.1080/13854046.2016.1277557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVandenbroucke JP, von Elm E, Altman DG, G\u0026oslash;tzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration. Int J Surg. 2014;12(12):1500\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDes Jarlais DC, Lyles C, Crepaz N, TREND Group. Improving the reporting quality of nonrandomized evaluations of behavioral and public health interventions: the TREND statement. Am J Public Health. 2004;94(3):361\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe EQUATOR, Network. CARITS \u0026ndash; Complete and Accurate Reporting of Interrupted Time Series studies [Internet]. [cited 2025 Dec 10]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-observational-studies/#CARITS\u003c/span\u003e\u003cspan address=\"https://www.equator-network.org/library/reporting-guidelines-under-development/reporting-guidelines-under-development-for-observational-studies/#CARITS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical research ed) [Internet]. 2021 Mar 29 [cited 2022 Aug 23];372. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/33782057/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/33782057/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwan K, Altman DG, Clarke M, Gamble C, Higgins JPT, Sterne JAC, et al. Evidence for the selective reporting of analyses and discrepancies in clinical trials: a systematic review of cohort studies of clinical trials. PLoS Med. 2014 June;11(6):e1001666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartung D, Zarin DA, Guise JM, McDonagh M, Paynter R, Helfand M. Reporting Discrepancies between the ClinicalTrials.gov Results Database and Peer Reviewed Publications. Ann Intern Med. 2014;160(7):477\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSiebert M, Caquelin L, Madera M, Acosta-Dighero R, Naudet F, Roqu\u0026eacute; M. Assessing the magnitude of changes from protocol to publication\u0026mdash;a survey on Cochrane and non-Cochrane Systematic Reviews. PeerJ. 2023;11:e16016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllers K, Hoffmann F, Mathes T, Pieper D. Systematic reviews with published protocols compared to those without: more effort, older search. J Clin Epidemiol. 2018;95:102\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInnocenti T, Feller D, Giagio S, Salvioli S, Minnucci S, Brindisino F, et al. Adherence to the PRISMA statement and its association with risk of bias in systematic reviews published in rehabilitation journals: A meta-research study. Braz J Phys Ther. 2022;26(5):100450.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoskinas X, Simes RJ, Martin AJ. Changes to design and analysis elements of research plans during randomised controlled trials in Australia. Med J Aust [Internet]. 2022 Sept 26 [cited 2025 Aug 12];217(10). Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mja.com.au/journal/2022/217/10/changes-design-and-analysis-elements-research-plans-during-randomised\u003c/span\u003e\u003cspan address=\"https://www.mja.com.au/journal/2022/217/10/changes-design-and-analysis-elements-research-plans-during-randomised\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"interrupted time series, reporting bias, statistical methods, methodology, statistical analysis, data collection, study design, non-randomised studies, discrepancies, deviations","lastPublishedDoi":"10.21203/rs.3.rs-8612197/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8612197/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Unexplained changes from a study’s analysis plan may increase the risk of bias in study results and undermine confidence in the findings. Discrepancies in methods between protocols and reports have not been examined in interrupted time series (ITS) studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We searched for peer-reviewed protocols of ITS studies in 22 databases, and results reports addressing each protocol’s primary research question. We compared 20 design and analysis items between protocols and their reports, classifying an item as ‘discrepant’ when the report provided different details about the item compared to the protocol. We judged a discrepancy as ‘potentially important’ if it could have potentially impacted the results or the study conclusion. We recorded if authors provided justifications for discrepancies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: From 4,590 abstracts, after excluding ongoing studies, we identified 120 eligible ITS protocols, from which 44 protocols (37%) had at least one corresponding results report. Information about handling the complexities of time-series data were frequently missing from the protocol or report, or both, for example, methods of adjusting for autocorrelation (77% of studies, 34/44) and seasonality (82%, 36/44). Potentially important discrepancies were common for eligibility criteria (43%, 19/44), overall length of time series (39%, 17/44), length of each time segment (45%, 20/44), effect measures (25%, 11/44) and ITS analysis methods (23%, 10/44). Among studies with important discrepancies, justifications were missing in 50%-100% of cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: \u0026nbsp;Study designs and analysis methods were often not, or insufficiently, reported in ITS studies. Changes to original analysis plans were also prevalent and often unjustified, precluding readers from judging the legitimacy of the changes. Protocols for ITS studies should provide detailed information about the design and analysis methods. Deviations from planned methods should be transparently reported with clear justifications.\u003c/p\u003e","manuscriptTitle":"Discrepancies in reporting of study design and analysis methods between protocols and reports of interrupted time series (ITS) studies: a methodological study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:40:46","doi":"10.21203/rs.3.rs-8612197/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-19T15:07:49+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T00:32:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T11:07:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T21:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96422010395017480020017016414238681785","date":"2026-01-28T02:13:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94696573888971095198692298524512100282","date":"2026-01-23T11:07:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254389383726030643038343660617335192286","date":"2026-01-23T08:44:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T23:00:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T10:10:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T05:49:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T05:48:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2026-01-15T15:50:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb100359-028c-425c-80f6-e854b5327f87","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T15:23:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:40:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8612197","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8612197","identity":"rs-8612197","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}