Factors influencing the time to ethics and governance approvals for clinical trials

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The time it takes to commence clinical trials at sites can be long, and ethics and governance approvals are key steps on the pathway to site activation. Methods This paper explores factors influencing the times to ethics approval, governance approval and site activation. Broadly, these comprised trial characteristics (disease area and trial phase), site characteristics (public or private ownership, country) and characteristics of the ethics and governance processes (scope guidelines, mutual acceptance requirements and triage of projects by risk). Median times were compared between site initiations that were and were not exposed to each characteristic using non-parametric tests in univariable and multivariable regressions. Results There were data from 150 site activations done across 91sites, 16 trials and 5 countries. The overall median time to activation was 234 days (range 74 to 657), with ethics approval taking a median of 48 days (0 to 369) and governance approval a median of 34 days (0 to 489). Both the univariable and multivariable analyses identified associations of disease area, particularly oncology (p univariable = 0.012, p multivariable = 0.044), use of scope guidelines (p < 0.001, p = 0.020) and use of a triage process (p < 0.001, 0.043) with shorter median times for governance approval. These characteristics (all p 0.054). The only factors associated with reduced overall time to site activation in both univariable and multivariable analyses were early trial phase (p < 0.001, p = 0.013) and mutual acceptance of ethics approvals (p = 0.031, p = 0.030). Interpretation Times to ethics and governance approvals were only one third of total trial start-up time. Factors influencing times to approval and activation were somewhat inconsistent across analyses, but it seems likely that the introduction of selected governance and ethics processes can reduce approval times. Figures Figure 1 INTRODUCTION Randomised controlled clinical trials are gold standard research investigations used to generate high-quality data about ways to prevent, detect or treat medical conditions. The evidence produced by clinical trials forms the basis for the development and implementation of new health interventions, procedures, and technologies, as well as clinical guidelines and government policy. In recent times industrialisation of the health research industry has seen clinical trials become important sources of employment and income generation for individuals, corporations, and government entities. Streamlined processes are now central to the clinical, commercial and policy success of trials. In conjunction with pressures for the rapid commencement of clinical trials is the need for oversight and regulation that can ensure the ethics and quality of the projects. Some serious breaches of medical ethics in research done during the last century have highlighted the need for careful ethical review. In parallel, the institutions responsible for the conduct of clinical trials have identified the need for better coordinated planning relating to how the research will be done in their organisations. For many decades, most jurisdictions doing clinical trials have required an ethics review to protect the dignity, rights, and welfare of research participants as well as a separate site governance review that defines how the research will be implemented. These two types of reviews are normally performed sequentially with the ethics review going first, but some oversight bodies will consider both concurrently. The ethics and governance infrastructure supporting clinical research has struggled to keep up with the numbers and complexity of clinical trials . For example, large-scale, multi-centre, international trials with pragmatic designs are mostly still required to operate using highly localised ethics and governance requirements with variable approval processes, fees, and timelines . The piecemeal fashion in which the sector has evolved in most jurisdictions has meant that the responsibilities of different parties are often poorly defined. Processes can be overlapping and bureaucratic requiring reduplication of effort, enormous resource, and extended timelines . These delays result in additional costs to researchers which impacts that jurisdictions competitiveness and attractiveness as a site of further research . As a response to these challenges, many governments, hospitals, and research system actors have developed processes that are designed to improve the speed and efficiency of clinical trial applications. A recent systematic review identified almost 100 reports of interventions targeting different aspects of clinical trial administration including 45 targeting ethics approval processes or governance arrangements. Amongst these interventions, the review identified ‘scope guidelines’ (these limited the numbers of ambiguities in the process and fixed timelines held review bodies to defined schedules), ‘streamlined approval’ (categorised submissions by risk and triaged their review), and ‘mutual recognition’ (where ethics committees acknowledged other committees prior reviews) as showing promise for improving ethics review processes for clinical trials and scope guidelines, streamlined approval, and coordinating bodies as having potential for enhancing governance processes. Clinical Research Organisations do trials in multiple sites across different jurisdictions and often use a clinical trial management system to record standard data about the passage of a trial through the various parts of the regulatory and review processes. In conjunction with data that describe the regulatory environment at each site, this provides an opportunity to explore objectively the association of different regulatory set-ups on the passage of clinical trials through the approval process. METHODS This was a retrospective analysis of the association between characteristics of the regulatory environment and the times to site activation, ethics approval and governance approval for clinical trials. The project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 29th of March 2021. The CONSORT checklist was used when writing the report . The guiding question of the study is “what are the factors associated with trial start-up times at sites and how large are their effects?” Included studies We used data from 150 site activation processes done for 16 different multi-centre clinical trials. These studies were included because meta data describing the start-up processes were readily available from the clinical trials management system of an Australia-based clinical research organization (George Clinical), with which the investigators had an established collaboration. There were no inclusion or exclusion criteria applied beyond the ability to access the metadata. Data extraction George Clinical team members (LH, MFD and LC) extracted standard data from the clinical trial management system (GrantPlan), the contracts database and other data repositories held by the company. The data were provided to a researcher external to George Clinical (AM) who de-identified the information by replacing the project name, principal investigator name and site name with unique identification numbers. A standard set of variables were extracted into an Excel database for each trial. AM also conducted desktop research relating to each trial site to collate standard information about the ethics and governance approval processes applicable to each trial. These data were variously obtained from online materials or through direct contact with staff at the trial site. Factors that might influence approval times The factors that were considered as potential determinants of site activation time fell into three categories - trial characteristics, site characteristics and characteristics of the ethics and governance processes. The latter variously operated at national, sub-national or site levels. Selection of characteristics for evaluation was based on two systematic reviews that assessed factors previously reported to influence time to trial start-up. , Site initiations were also grouped into pre 2020 and post 2020 groups to explore the impact of COVID 19. The key trial characteristics considered were the disease area (renal; oncology; neurology; endocrinology; paediatric) and the trial phase (1, 2, 3 or 4) and the site characteristics recorded were the ownership status (public or private) and the country (Australia, Hong Kong, Korea New Zealand, and Taiwan). The characteristics relating to the ethics review process were the use of scope guidelines by the ethics committee; a requirement for mutual acceptance of other ethics committee findings; and an ethics process that triaged applications as low, medium, or high risk. The characteristics relating to the governance review were the use of scope guidelines by the governance body; and a governance approval process that triaged applications as low, medium, or high risk. Outcomes The primary outcome was the time taken to achieve site activation defined as the date that ‘site activation’ was recorded in the clinical trial management system. The date of initial site contact made by the clinical research organisation was used as the date at which the trial start-up process was first recorded in the system. For 19 trials without a documented ‘site activation’ date the date of site activation was imputed as the date the first patient was recruited. The key secondary outcomes were the time from submitting to obtaining ethics approval and the time from submitting to attaining governance approval. Analysis The primary analysis was based on data from 150 site activations including 19 for which missing ‘site activation’ dates were imputed as the date of first patient recruitment. Since the distributions of the times across the included trials were noted to be substantively right-skewed, summary data for site activation, ethics approval and governance approval were reported as medians and ranges. Overall median times and ranges were first summarised for each outcome with all data available and then summarised separately for each subgroup for factors of interest. Differences in the median times across each subset for factors of interest were tested first using univariable regressions and non-parametric tests (Kruskal-Wallis rank sum tests or Wilcoxon rank sum tests) on log-transformed outcome data. We then repeated the analyses using multiple regression methods including all the variables from the univariable analysis, to assess the joint effects of the exposures. Records with missing or unknown values were excluded from relevant analyses with, for example, analysis of the effects of public versus private ownership restricted to Australia because this information could not be obtained for overseas sites. For the analyses of factors influencing time to site activation, a subsidiary analysis was done including only the 131 trials for which a date of site activation was recorded, without the 19 trials with imputed data (Supplementary Table 1). The data was also divided depending upon the year at which trial start-up was commenced (Jan 2014- Dec 2019 versus Jan 2020-Dec 2022) to test for effects of the COVID-19 pandemic. P-values < 0.05 were considered statistically significant. All analyses were done using R version 4.1.0 and RStudio 2022.07.2 Build 576 [ref]. Packages “gtsummary” [ref] and “ggplot2” [ref] were used for summarizing the data. RESULTS There were data describing 150 instances of site activation done at 91 different sites in 5 different countries (Table 1 ). Time to ‘site activation’ was available for 131 clinical trial initiations with data imputed for the other 19 instances using the date of ‘first patient recruited’. Time to ethics committee approval was available for 150 and time to governance approval was available for 145. The most frequent disease area was kidney disease (36%) followed by oncology (27%), endocrinology (16%), neurology (15%) and paediatrics (6%). Phase 3 trials were the most common (65%) followed by phase 2 (18%) phase 1 (11%) and the remainder were registry studies. The data were drawn predominantly from public sector institutions (72%). There were 84% of site activations done in Australia with the remainder done in Hong Kong, Korea New Zealand, and Taiwan. Overall, there were 73% of site activations exposed to one or more forms of ethics interventions and 75% exposed to one or more forms of governance intervention. The most frequently applied ethics intervention was triaging of studies into low, medium, or high risk applications (64%), and this was also the most frequently applied governance intervention (74%). The majority of the site activations commenced before COVID-19 emerged (76%) with the remainder done during or after the pandemic. Time to site activation The overall median time to site activation was 234 days with a range that extended from 74 to 657 days (Fig. 1 and Table 2 ). In the univariable analyses, shorter time to site activations was associated with earlier phase trials, (p = 0.001), the country in which the trial was done (p = 0.039), private ownership of the centre conducting the trial (p = 0.039), provisions to allow for the mutual acceptance of other ethics committee approval (p = 0.031) and initiation during or after the COVID-19 pandemic (p = 0.007). In the multivariable analysis these associations persisted for only earlier trial phase (p = 0.013) and the presence of mutual acceptance of ethics provisions (p = 0.030). Disease area was significantly associated with time to site activation in the multivariable analyses alone (p = 0.026) with a longer median duration for neurology trials. Repeating the analysis without the 19 site initiations for which site activation date was imputed (Supplementary table 1) showed broadly comparable results except that provisions to allow for the mutual acceptance of other ethics committee approval was non-significant in the multivariable analyses, and neurology trials switch from having the longest to the shortest site activation times. Time to ethics approval The median time to ethics approval from ethics submission was 48 days (range 0 to 369 days) (Fig. 1 , Table 3 ) with the zero-day approval times reflecting the impact of mutual acceptance schemes on two trial initiations. In the univariable analyses shorter time to ethics approval was associated with early trial phase (p = 0.028), disease area (particularly oncology) (p < 0.001), the use of scope guidelines (p < 0.001 univariable), mutual acceptance provisions (p < 0.001 univariable) and triaging according to risk (p 0.054). Time to governance approval The median time to achieve governance approval was 34 days (range 0 to 489) (Fig. 1 , Table 4 ). Shorter time to approval was associated with disease area (particularly oncology) (p = 0.012), use of scope guidelines for governance review (p < 0.001) and a process for triaging review based on risk (p < 0.001). All these findings persisted in the multivariable analysis (all p < 0.032). In addition, in the multivariable analysis alone, studies done during or after the COVID-19 pandemic had a longer time to governance approval that did trials approved pre-COVID (p = 0.013). DISCUSSION There were multiple associations of the characteristics of trials, sites, and review processes with times to regulatory approval and site activation. In terms of the process characteristics potentially modifiable by institutions there was strong evidence that streamlined governance processes could reduce governance approval times. Corresponding evidence for benefits from streamlining ethics processes was more limited though mutual acceptance of ethics review outcomes was the only process intervention associated with reduced overall time to site activation. In general, there was a disconnect with benefits of process interventions for ethics or governance timelines not translating into reduced overall times for site activation. This is likely a consequence of the fairly short median times for achieving governance (median 34 days) and ethics (median 48 days) approvals which constituted only about one third of the much longer median time to site activation (234 days). Ethics and governance approval times are widely considered to be barriers to rapid trial start-up, but these analyses show that other processes in the pathway to site start-up take much more time to be achieved. So, while enhancing ethics and governance interventions can play a role in speeding site activation, the average impact may be limited. Further work to identify whether it is related factors such as the preparation of applications for ethics and governance reviews, or separate issues such as budget negotiations, contract finalisation and other site processes that accrue most of the additional required to achieve site activation. The positive association of governance interventions with shorter time to governance approval observed in these analyses supports the implementation of governance processes that define the scope of governance review (resulting in a median difference of 51 days) and triage projects according to risk (resulting in a median difference of 57 days). The alignment of the favourable findings for these interventions in the current analyses with positive findings for these types of interventions in prior reports provides further support for their likely value. The results for ethics interventions on ethics approval times were less compelling with considerable inconsistency across the findings for the univariable and multivariable analyses. The observation that schemes that support mutual acceptance of ethics review were associated with shorter overall time to site activation provides significant additional support for a likely benefit from this strategy. Prior research has also identified mutual acceptance of ethics review as a priority ethics intervention and the totality of the evidence across this study and prior investigations suggests it is likely to be effective. Based on the current data, there remains greater uncertainty about the value of scope guidelines and triaging of ethics applications though both are inherently appealing. There were several non-modifiable characteristics of studies, like trial phase and disease area, for which associations with time to approval were also observed. Deeper investigation of the reasons why particular types of projects were associated with shorter approval times may provide insight into ways that approval times and site start-up may be reduced. For example, it may be that some aspect of trial start-up has been optimised by an initiative undertaken in a specific disease area, and this could be generalised to other specialities. The findings regarding the effects of the COVID-19 pandemic on approval times in this study were inconclusive. Anecdotal reports suggest that ethics and governance interventions to achieve rapid approval and start-up of COVID-19 studies dramatically reduced timelines for selected projects but it is unclear whether these changes provided broader system gains or were at the expense of non-COVID-19 projects. Systematic quantitative assessments are required, and this report does not provide insight into this question. A key strength of this study was the ability to more directly and objectively quantify the association of potential determinants of time to trial start-up identified in prior qualitative and semi-quantitative studies. The joint use of univariable and multivariable analyses provided in-depth insight into the likely robustness of the findings and the separate assessment of times to governance and ethics approvals, as well as overall time to trial start-up, enabled us to place the findings in a broader context. Key challenges were the relatively small size of the dataset and the incomplete data for some site initiations which limited statistical power and meant that clustering at each site was unobservable due to the low numbers of trials performed at each site. It is also possible that not all-important characteristics of site initiation processes were captured, and residual confounding is possible. While the data provided here are valuable themselves, the study highlights the potential for future larger analyses, with data from more than one research institution, which would be relatively straightforward to repeat them on other datasets if research operations partners could be identified. There may also be value in exploring the outliers in the dataset using qualitative research methods to identify factors that led to extremely rapid or extremely delayed approvals. In conclusion, although ethics and governance reviews do not represent the majority of approval time, these analyses support the introduction of streamlined ethics and governance processes by governments and health departments seeking to reduce the time to clinical trial start-up. The introduction of process enhancements should be accompanied by the collection, analysis, and reporting of meta-data to objectively quantify impact. Table 1 Characteristics of 150 trial start-ups for the 16 multicentre trials evaluated Number of start-ups (n = 150) Phase 1 17 2 27 3 98 4 8 Disease area of trial Renal 54 Oncology 40 Endocrinology 24 Neurology 22 Paediatrics 10 Country Australia 126 Korea 10 Hong Kong 5 Taiwan 5 New Zealand 4 Trial site ownership Public 108 Private 18 Unknown 24 Scope guidelines for the ethics review Yes 116 No 34 Mutual acceptance of other ethics committee approval Yes 79 No 55 Unknown 16 Applications triaged low/medium/high risk for ethics review Yes 96 No 49 Unknown 5 Scope guidelines used for the governance review Yes 112 No 38 Applications triaged low/medium/high risk for governance review Yes 111 No 31 Unknown 8 COVID-19 pandemic Pre-pandemic (Jan 2014- Dec 2019) During or after pandemic (Jan 2020-Dec 2022) 114 36 Table 2 Factors influencing time to site activation (days) for 150 sites (including 19 imputed) initiations. Median (range) Univariable p-value* Multivariable p-value** Overall (n = 150) 234 (74, 657) Phase 1 (n = 17) 160 (114, 269) <0.001* 2 (n = 27) 192 (74, 657) 0.013** 3 (n = 98) 250 (106, 617) 4 (n = 8) 289 (85, 350) Disease area of trial Renal (n = 54) 230 (74, 617) 0.7* Oncology (n = 40) 249 (94, 346) 0.026** Endocrinology (n = 24) 244 (106, 372) Neurology (n = 22) 294 (82, 657) Paediatrics (n = 10) 226 (138, 519) Country Australia (n = 126) 236 (74, 657) 0.039* Korea (n = 10) 230 (211, 312) NA** Hong Kong (n = 5) 395 (262, 426) Taiwan (n = 5) 174 (154, 252) New Zealand (n = 4) 184 (177, 196) Trial site ownership Public (n = 108) 248 (74, 657) 0.039* Private (n = 18) 188 (114, 484) 0.8** N/A (n = 24) 230 (154, 426) Mutual acceptance of other ethics committee approvals Yes (n = 79) 230 (82, 601) 0.031* No (n = 55) 236 (74, 657) 0.030** Unknown (n = 16) 248 (94, 346) Triage by low, medium, or high risk for ethics review Yes (n = 96) 250 (74, 657) >0.9* No (n = 49) 232 (138, 484) 0.3** Unknown (n = 5) 150 (94, 224) Triage by low, medium, or high risk for governance review Yes (n = 111) 236 (74, 617) >0.9* No (n = 31) 224 (94, 657) 0.8** Scope guidelines for the ethics review Yes (n = 116) 233 (74, 657) 0.2* No (n = 34) 235 (144, 484) 0.6** Unknown (n = 8) 178 (114, 252) Scope guidelines for the governance review Yes (n = 112) 234 (74, 617) >0.9* No (n = 38) 247 (114, 657) >0.3** COVID-19 pandemic Pre-pandemic (n = 114) 248 (82, 657) 0.007* During or after pandemic (n = 36) 192 (74, 617) 0.9** * p-value from univariable comparison ** p-value from multivariable comparison that included disease area; phase; ownership status (N/A data removed from ownership status); scope guidelines by the ethics committee; mutual acceptance of other ethics committee findings; an ethics process that triaged applications as low, medium, or high risk; the use of scope guidelines by the governance body; a governance approval process that triaged applications as low, medium or high risk; and pre- or post-COVID in the regression model. NA = not applicable because only one country (Australia) had data to enable multivariable analyses. Table 3 Factors influencing median time from ethics submission to ethics approval (days) for 150 site activations. Median (range) Univariable p-value* Multivariable p-value** Overall 48 (0, 369) Phase 1 (n = 17) 15 (5, 106) 0.028* 2 (n = 27) 41 (16, 139) 0.7** 3 (n = 98) 61 (0, 369) 4 (n = 8) 49 (2, 49) Disease area of trial Renal (n = 54) 49 (2, 139) < 0.001* Oncology (n = 40) 21 (3, 106) 0.054** Endocrinology (n = 24) 97 (0, 203) Neurology (n = 22) 41 (16, 99) Paediatrics (n = 10) 96 (20, 369) Country Australia (n = 126) 48 (0, 369) 0.094* Korea (n = 10) 46 (11, 75) NA** Hong Kong (n = 5) 86 (35, 105) Taiwan (n = 5) 32 (32, 32) New Zealand (n = 4) 63 (63, 63) Trial site ownership Public (n = 108) 48 (0, 369) > 0.9* Private (n = 18) 46 (8, 215) 0.2** Unknown (n = 24) 54 (11, 105) Scope guidelines for ethics review Yes (n = 116) 38 (0, 369) < 0.001* No (n = 34) 88 (41, 215) 0.2** Mutual acceptance of other ethics committee approvals Yes (n = 79) 34 (0, 369) < 0.001* No (n = 55) 67 (13, 215) 0.65** Unknown (n = 16) 18 (6, 125) Triage by low, medium, or high risk Yes (n = 96) 38 (0, 369) < 0.001* No (n = 49) Unknown (n = 5) 67 (3, 215) 31 (15, 99) 0.4** COVID-19 pandemic Pre-pandemic (n = 114) 44 (0, 369) 0.5* During or after pandemic (n = 36) 49 (2, 139) 0.8** * p-value from univariable comparison ** p-value from multivariable comparison that included phase; disease area; country; trial site ownership status (N/A data removed from ownership status); scope guidelines by the ethics committee; mutual acceptance of other ethics committee findings; an ethics process that triaged applications as low, medium, or high risk; and pre- or post-COVID in the regression model. NA = not applicable because only one country (Australia) had data to enable multivariable analyses. Table 4 Factors influencing time to governance submission to governance approval (days) for 145 site activations Median (range) Univariable p-value* Multivariable p-value** Overall 34 (0, 489) Phase 1 (n = 17) 39 (0, 89) 0.2* 2 (n = 27) 45 (1, 140) 0.014** 3 (n = 93) 33 (0, 489) 4 (n = 8) 18 (12, 99) Disease area of trial Renal (n = 49) 37 (0, 489) 0.012* Oncology (n = 40) 17 (0, 112) 0.044** Endocrinology (n = 24) 41 (3, 192) Neurology (n = 22) 47 (1, 140) Paediatrics (n = 10) 40 (21, 433) Country Australia (n = 126) 34 (0, 489) 0.094* Korea (n = 10) 10 (0, 102) NA** Hong Kong (n = 5) 62 (9, 87) Taiwan (n = 0) - New Zealand (n = 4) 30 (7, 56) Trial site ownership Public (n = 108) 35 (0, 489) 0.3* Private (n = 18) 28 (3, 77) 0.032** Unknown (n = 19) 14 (0, 102) Scope guidelines used for the governance review Yes (n = 110) 28 (0, 489) < 0.001* No (n = 35 79 (3, 135) 0.020** Triage by low, medium, or high risk Yes (n = 111) 24 (0, 489) < 0.001* No (n = 31) 81 (29, 135) 0.043** Unknown (n = 3) 10 (6, 53) COVID-19 pandemic Pre-pandemic (n = 109) 31 (0, 192) 0.12* During or after pandemic (n = 36) 41 (7, 489) 0.013** * p-value from univariable comparison ** p-value from multivariable comparison that included disease area; phase; trial site ownership status (N/A data removed from ownership status); the use of scope guidelines by the governance body; a governance approval process that triaged applications as low, medium, or high risk; and pre- or post-COVID in the regression model. NA = not applicable because only one country (Australia) had data to enable multivariable analyses. Declarations Ethics approval The project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 29 th of March 2021. Consent for publication Not required for this publication Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study did not attract any external funding or grants. Authors Contributions SC, AM, LH, SJ, RH & BN made substantial contributions to the conception and design of the work. SC and AM acquired and processed the data, LH, primarily interpreted the data assisted by BN and SC. BN, SC, SJ, RH drafted and substantially revised the work. SC as corresponding author liaised with each author ensuring their input and revisions were accounted for. Acknowledgements Lyndal Hones, George Clinical, Sydney, Australia Luc Cambon, George Clinical, Sydney, Australia Martin Febbo-Dobson, George Clinical, Sydney, Australia James Chung, George Clinical, Sydney, Australia References NHMRC (National Health and Medical Research Council), Australian Clinical Trials, (2021) https://www.australianclinicaltrials.gov.au/what-clinical-trial DOH (Department of Health), (2021) Clinical trials, https://www1.health.gov.au/internet/main/publishing.nsf/Content/Clinical-Trials Rothman DJ. Ethics and human experimentation. N Engl J Med1987;317:1195-1199 Junod, S.W., (2019) FDA and Clinical Drug Trials: A Short History, US Food and Drug Administration, Maryland https://www.fda.gov/media/110437/download Whitney SN, Schneider CE (2011) A method to estimate the cost in lives of ethics board review of biomedical research. 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CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials Crosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. https://doi.org/10.1057/s41599-022-01269-3 Crosby S, Rajadurai E, Jan S, Neal B, Holden R (2022) The effects on clinical trial activity of direct funding and taxation policy interventions made by government: A systematic review. 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Crosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. https://doi.org/10.1057/s41599-022-01269-3 Supplementary Files Supplementarytable1.docx Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2023 Read the published version in Trials → Version 1 posted Editorial decision: Major revision 28 Aug, 2023 Reviewers agreed at journal 29 Jul, 2023 Reviewers invited by journal 28 Jul, 2023 Editor assigned by journal 16 Jul, 2023 First submitted to journal 19 Jun, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3047964","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":222363819,"identity":"7f00b50b-00ab-4333-bd00-42ff7f9ae21a","order_by":0,"name":"Sam Crosby","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-8217-2540","institution":"The George Institute for Global Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sam","middleName":"","lastName":"Crosby","suffix":""},{"id":222363820,"identity":"846f7e12-348d-404b-b8a1-63406c9142f1","order_by":1,"name":"Adriana Malavisi1","email":"","orcid":"","institution":"The George Institute for Global Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Malavisi1","suffix":""},{"id":222363821,"identity":"8d63f9f4-0ff4-4646-8d5b-85b0680a2fe0","order_by":2,"name":"Liping Huang","email":"","orcid":"","institution":"The George Institute for Global Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Huang","suffix":""},{"id":222363822,"identity":"49325d9e-7a71-4ae0-9ee6-71c7fd5ffaac","order_by":3,"name":"Stephen Jan","email":"","orcid":"","institution":"The George Institute for Global Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"","lastName":"Jan","suffix":""},{"id":222363823,"identity":"d92cd7fe-4873-4aee-8bb4-3d11cc60eab9","order_by":4,"name":"Richard Holden","email":"","orcid":"","institution":"UNSW Business School: University of New South Wales Business School","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Holden","suffix":""},{"id":222363824,"identity":"e3c07e41-fbd0-4751-aa56-0a4a201d4186","order_by":5,"name":"Bruce Neal","email":"","orcid":"","institution":"The George Institute for Global Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bruce","middleName":"","lastName":"Neal","suffix":""}],"badges":[],"createdAt":"2023-06-11 02:17:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3047964/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3047964/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13063-023-07802-2","type":"published","date":"2023-12-01T15:00:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41023674,"identity":"5bfecae6-b87f-4cf2-aec1-234eee2246dc","added_by":"auto","created_at":"2023-08-03 15:32:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25132,"visible":true,"origin":"","legend":"\u003cp\u003eOverall median (range) times for site activation, ethics approval and governance approval\u003c/p\u003e\n\u003cp\u003eThere were 19 site activation times where the date of ‘site activation’ was not recorded and was imputed as the date ‘first patient recruited’.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3047964/v1/b658779bb7feb74a9bc1c080.png"},{"id":47561007,"identity":"d8ec639a-e33d-409c-8717-a57416a5b85e","added_by":"auto","created_at":"2023-12-04 15:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":604892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3047964/v1/62a444bd-c3be-4617-9714-003c050fcd4b.pdf"},{"id":41023673,"identity":"c8039121-fc0c-4861-97a6-522cf097bcfd","added_by":"auto","created_at":"2023-08-03 15:32:28","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30831,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3047964/v1/448979bed0b403f6a59475cd.docx"}],"financialInterests":"","formattedTitle":"Factors influencing the time to ethics and governance approvals for clinical trials","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRandomised controlled clinical trials are gold standard research investigations used to generate high-quality data about ways to prevent, detect or treat medical conditions.\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e The evidence produced by clinical trials forms the basis for the development and implementation of new health interventions, procedures, and technologies, as well as clinical guidelines and government policy. In recent times industrialisation of the health research industry has seen clinical trials become important sources of employment and income generation for individuals, corporations, and government entities.\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e Streamlined processes are now central to the clinical, commercial and policy success of trials.\u003c/p\u003e \u003cp\u003eIn conjunction with pressures for the rapid commencement of clinical trials is the need for oversight and regulation that can ensure the ethics and quality of the projects. Some serious breaches of medical ethics in research done during the last century\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e have highlighted the need for careful ethical review. In parallel, the institutions responsible for the conduct of clinical trials have identified the need for better coordinated planning relating to how the research will be done in their organisations. For many decades, most jurisdictions doing clinical trials have required an ethics review to protect the dignity, rights, and welfare of research participants as well as a separate site governance review that defines how the research will be implemented.\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e These two types of reviews are normally performed sequentially with the ethics review going first, but some oversight bodies will consider both concurrently.\u003c/p\u003e \u003cp\u003eThe ethics and governance infrastructure supporting clinical research has struggled to keep up with the numbers and complexity of clinical trials\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e. For example, large-scale, multi-centre, international trials with pragmatic designs are mostly still required to operate using highly localised ethics and governance requirements with variable approval processes, fees, and timelines\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e. The piecemeal fashion in which the sector has evolved in most jurisdictions has meant that the responsibilities of different parties are often poorly defined. Processes can be overlapping and bureaucratic requiring reduplication of effort, enormous resource, and extended timelines\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e. These delays result in additional costs to researchers which impacts that jurisdictions competitiveness and attractiveness as a site of further research\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e.\u003c/p\u003e \u003cp\u003eAs a response to these challenges, many governments, hospitals, and research system actors have developed processes that are designed to improve the speed and efficiency of clinical trial applications. A recent systematic review identified almost 100 reports of interventions targeting different aspects of clinical trial administration including 45 targeting ethics approval processes or governance arrangements.\u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003e Amongst these interventions, the review identified \u0026lsquo;scope guidelines\u0026rsquo; (these limited the numbers of ambiguities in the process and fixed timelines held review bodies to defined schedules), \u0026lsquo;streamlined approval\u0026rsquo; (categorised submissions by risk and triaged their review), and \u0026lsquo;mutual recognition\u0026rsquo; (where ethics committees acknowledged other committees prior reviews) as showing promise for improving ethics review processes for clinical trials and scope guidelines, streamlined approval, and coordinating bodies as having potential for enhancing governance processes.\u003c/p\u003e \u003cp\u003eClinical Research Organisations do trials in multiple sites across different jurisdictions and often use a clinical trial management system to record standard data about the passage of a trial through the various parts of the regulatory and review processes. In conjunction with data that describe the regulatory environment at each site, this provides an opportunity to explore objectively the association of different regulatory set-ups on the passage of clinical trials through the approval process.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis was a retrospective analysis of the association between characteristics of the regulatory environment and the times to site activation, ethics approval and governance approval for clinical trials. The project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 29th of March 2021. The CONSORT checklist was used when writing the report\u003ca class=\"FNLink\" href=\"#Fn10\" id=\"#FNLinkFn10\"\u003e\u003c/a\u003e. The guiding question of the study is \u0026ldquo;what are the factors associated with trial start-up times at sites and how large are their effects?\u0026rdquo;\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIncluded studies\u003c/h2\u003e \u003cp\u003eWe used data from 150 site activation processes done for 16 different multi-centre clinical trials. These studies were included because meta data describing the start-up processes were readily available from the clinical trials management system of an Australia-based clinical research organization (George Clinical), with which the investigators had an established collaboration. There were no inclusion or exclusion criteria applied beyond the ability to access the metadata.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eGeorge Clinical team members (LH, MFD and LC) extracted standard data from the clinical trial management system (GrantPlan), the contracts database and other data repositories held by the company. The data were provided to a researcher external to George Clinical (AM) who de-identified the information by replacing the project name, principal investigator name and site name with unique identification numbers. A standard set of variables were extracted into an Excel database for each trial. AM also conducted desktop research relating to each trial site to collate standard information about the ethics and governance approval processes applicable to each trial. These data were variously obtained from online materials or through direct contact with staff at the trial site.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eFactors that might influence approval times\u003c/h2\u003e \u003cp\u003eThe factors that were considered as potential determinants of site activation time fell into three categories - trial characteristics, site characteristics and characteristics of the ethics and governance processes. The latter variously operated at national, sub-national or site levels. Selection of characteristics for evaluation was based on two systematic reviews that assessed factors previously reported to influence time to trial start-up.\u003ca class=\"FNLink\" href=\"#Fn11\" id=\"#FNLinkFn11\"\u003e\u003c/a\u003e,\u003ca class=\"FNLink\" href=\"#Fn12\" id=\"#FNLinkFn12\"\u003e\u003c/a\u003e Site initiations were also grouped into pre 2020 and post 2020 groups to explore the impact of COVID 19.\u003c/p\u003e \u003cp\u003eThe key trial characteristics considered were the disease area (renal; oncology; neurology; endocrinology; paediatric) and the trial phase (1, 2, 3 or 4) and the site characteristics recorded were the ownership status (public or private) and the country (Australia, Hong Kong, Korea New Zealand, and Taiwan). The characteristics relating to the ethics review process were the use of scope guidelines by the ethics committee; a requirement for mutual acceptance of other ethics committee findings; and an ethics process that triaged applications as low, medium, or high risk. The characteristics relating to the governance review were the use of scope guidelines by the governance body; and a governance approval process that triaged applications as low, medium, or high risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the time taken to achieve site activation defined as the date that \u0026lsquo;site activation\u0026rsquo; was recorded in the clinical trial management system. The date of initial site contact made by the clinical research organisation was used as the date at which the trial start-up process was first recorded in the system. For 19 trials without a documented \u0026lsquo;site activation\u0026rsquo; date the date of site activation was imputed as the date the first patient was recruited. The key secondary outcomes were the time from submitting to obtaining ethics approval and the time from submitting to attaining governance approval.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis\u003c/h2\u003e \u003cp\u003eThe primary analysis was based on data from 150 site activations including 19 for which missing \u0026lsquo;site activation\u0026rsquo; dates were imputed as the date of first patient recruitment. Since the distributions of the times across the included trials were noted to be substantively right-skewed, summary data for site activation, ethics approval and governance approval were reported as medians and ranges. Overall median times and ranges were first summarised for each outcome with all data available and then summarised separately for each subgroup for factors of interest. Differences in the median times across each subset for factors of interest were tested first using univariable regressions and non-parametric tests (Kruskal-Wallis rank sum tests or Wilcoxon rank sum tests) on log-transformed outcome data. We then repeated the analyses using multiple regression methods including all the variables from the univariable analysis, to assess the joint effects of the exposures. Records with missing or unknown values were excluded from relevant analyses with, for example, analysis of the effects of public versus private ownership restricted to Australia because this information could not be obtained for overseas sites. For the analyses of factors influencing time to site activation, a subsidiary analysis was done including only the 131 trials for which a date of site activation was recorded, without the 19 trials with imputed data (Supplementary Table\u0026nbsp;1). The data was also divided depending upon the year at which trial start-up was commenced (Jan 2014- Dec 2019 versus Jan 2020-Dec 2022) to test for effects of the COVID-19 pandemic. P-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. All analyses were done using R version 4.1.0 and RStudio 2022.07.2 Build 576 [ref]. Packages \u0026ldquo;gtsummary\u0026rdquo; [ref] and \u0026ldquo;ggplot2\u0026rdquo; [ref] were used for summarizing the data.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThere were data describing 150 instances of site activation done at 91 different sites in 5 different countries (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Time to \u0026lsquo;site activation\u0026rsquo; was available for 131 clinical trial initiations with data imputed for the other 19 instances using the date of \u0026lsquo;first patient recruited\u0026rsquo;. Time to ethics committee approval was available for 150 and time to governance approval was available for 145. The most frequent disease area was kidney disease (36%) followed by oncology (27%), endocrinology (16%), neurology (15%) and paediatrics (6%). Phase 3 trials were the most common (65%) followed by phase 2 (18%) phase 1 (11%) and the remainder were registry studies. The data were drawn predominantly from public sector institutions (72%). There were 84% of site activations done in Australia with the remainder done in Hong Kong, Korea New Zealand, and Taiwan. Overall, there were 73% of site activations exposed to one or more forms of ethics interventions and 75% exposed to one or more forms of governance intervention. The most frequently applied ethics intervention was triaging of studies into low, medium, or high risk applications (64%), and this was also the most frequently applied governance intervention (74%). The majority of the site activations commenced before COVID-19 emerged (76%) with the remainder done during or after the pandemic.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTime to site activation\u003c/h2\u003e \u003cp\u003eThe overall median time to site activation was 234 days with a range that extended from 74 to 657 days (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the univariable analyses, shorter time to site activations was associated with earlier phase trials, (p\u0026thinsp;=\u0026thinsp;0.001), the country in which the trial was done (p\u0026thinsp;=\u0026thinsp;0.039), private ownership of the centre conducting the trial (p\u0026thinsp;=\u0026thinsp;0.039), provisions to allow for the mutual acceptance of other ethics committee approval (p\u0026thinsp;=\u0026thinsp;0.031) and initiation during or after the COVID-19 pandemic (p\u0026thinsp;=\u0026thinsp;0.007). In the multivariable analysis these associations persisted for only earlier trial phase (p\u0026thinsp;=\u0026thinsp;0.013) and the presence of mutual acceptance of ethics provisions (p\u0026thinsp;=\u0026thinsp;0.030). Disease area was significantly associated with time to site activation in the multivariable analyses alone (p\u0026thinsp;=\u0026thinsp;0.026) with a longer median duration for neurology trials.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRepeating the analysis without the 19 site initiations for which site activation date was imputed (Supplementary table 1) showed broadly comparable results except that provisions to allow for the mutual acceptance of other ethics committee approval was non-significant in the multivariable analyses, and neurology trials switch from having the longest to the shortest site activation times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eTime to ethics approval\u003c/h2\u003e \u003cp\u003eThe median time to ethics approval from ethics submission was 48 days (range 0 to 369 days) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) with the zero-day approval times reflecting the impact of mutual acceptance schemes on two trial initiations. In the univariable analyses shorter time to ethics approval was associated with early trial phase (p\u0026thinsp;=\u0026thinsp;0.028), disease area (particularly oncology) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the use of scope guidelines (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 univariable), mutual acceptance provisions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 univariable) and triaging according to risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 univariable). None of these findings persisted in the multivariable analyses (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.054).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTime to governance approval\u003c/h2\u003e \u003cp\u003eThe median time to achieve governance approval was 34 days (range 0 to 489) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Shorter time to approval was associated with disease area (particularly oncology) (p\u0026thinsp;=\u0026thinsp;0.012), use of scope guidelines for governance review (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a process for triaging review based on risk (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). All these findings persisted in the multivariable analysis (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.032). In addition, in the multivariable analysis alone, studies done during or after the COVID-19 pandemic had a longer time to governance approval that did trials approved pre-COVID (p\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThere were multiple associations of the characteristics of trials, sites, and review processes with times to regulatory approval and site activation. In terms of the process characteristics potentially modifiable by institutions there was strong evidence that streamlined governance processes could reduce governance approval times. Corresponding evidence for benefits from streamlining ethics processes was more limited though mutual acceptance of ethics review outcomes was the only process intervention associated with reduced overall time to site activation. In general, there was a disconnect with benefits of process interventions for ethics or governance timelines not translating into reduced overall times for site activation. This is likely a consequence of the fairly short median times for achieving governance (median 34 days) and ethics (median 48 days) approvals which constituted only about one third of the much longer median time to site activation (234 days).\u003c/p\u003e\n\u003cp\u003eEthics and governance approval times are widely considered to be barriers to rapid trial start-up, but these analyses show that other processes in the pathway to site start-up take much more time to be achieved. So, while enhancing ethics and governance interventions can play a role in speeding site activation, the average impact may be limited. Further work to identify whether it is related factors such as the preparation of applications for ethics and governance reviews, or separate issues such as budget negotiations, contract finalisation and other site processes that accrue most of the additional required to achieve site activation.\u003c/p\u003e\n\u003cp\u003eThe positive association of governance interventions with shorter time to governance approval observed in these analyses supports the implementation of governance processes that define the scope of governance review (resulting in a median difference of 51 days) and triage projects according to risk (resulting in a median difference of 57 days). The alignment of the favourable findings for these interventions in the current analyses with positive findings for these types of interventions in prior reports provides further support for their likely value.\u003c/p\u003e\n\u003cp\u003eThe results for ethics interventions on ethics approval times were less compelling with considerable inconsistency across the findings for the univariable and multivariable analyses. The observation that schemes that support mutual acceptance of ethics review were associated with shorter overall time to site activation provides significant additional support for a likely benefit from this strategy. Prior research has also identified mutual acceptance of ethics review as a priority ethics intervention and the totality of the evidence across this study and prior investigations suggests it is likely to be effective. Based on the current data, there remains greater uncertainty about the value of scope guidelines and triaging of ethics applications though both are inherently appealing.\u003c/p\u003e\n\u003cp\u003eThere were several non-modifiable characteristics of studies, like trial phase and disease area, for which associations with time to approval were also observed. Deeper investigation of the reasons why particular types of projects were associated with shorter approval times may provide insight into ways that approval times and site start-up may be reduced. For example, it may be that some aspect of trial start-up has been optimised by an initiative undertaken in a specific disease area, and this could be generalised to other specialities. The findings regarding the effects of the COVID-19 pandemic on approval times in this study were inconclusive. Anecdotal reports suggest that ethics and governance interventions to achieve rapid approval and start-up of COVID-19 studies dramatically reduced timelines for selected projects but it is unclear whether these changes provided broader system gains or were at the expense of non-COVID-19 projects. Systematic quantitative assessments are required, and this report does not provide insight into this question.\u003c/p\u003e\n\u003cp\u003eA key strength of this study was the ability to more directly and objectively quantify the association of potential determinants of time to trial start-up identified in prior qualitative and semi-quantitative studies. \u0026nbsp;The joint use of univariable and multivariable analyses provided in-depth insight into the likely robustness of the findings and the separate assessment of times to governance and ethics approvals, as well as overall time to trial start-up, enabled us to place the findings in a broader context. Key challenges were the relatively small size of the dataset and the incomplete data for some site initiations which limited statistical power and meant that clustering at each site was unobservable due to the low numbers of trials performed at each site. It is also possible that not all-important characteristics of site initiation processes were captured, and residual confounding is possible. While the data provided here are valuable themselves, the study highlights the potential for future larger analyses, with data from more than one research institution, which would be relatively straightforward to repeat them on other datasets if research operations partners could be identified. There may also be value in exploring the outliers in the dataset using qualitative research methods to identify factors that led to extremely rapid or extremely delayed approvals.\u003c/p\u003e\n\u003cp\u003eIn conclusion, although ethics and governance reviews do not represent the majority of approval time, these analyses support the introduction of streamlined ethics and governance processes by governments and health departments seeking to reduce the time to clinical trial start-up. The introduction of process enhancements should be accompanied by the collection, analysis, and reporting of meta-data to objectively quantify impact.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of 150 trial start-ups for the 16 multicentre trials evaluated\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of start-ups (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhase\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease area of trial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrinology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaediatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong Kong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Zealand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial site ownership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines for the ethics review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutual acceptance of other ethics committee approval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eApplications triaged low/medium/high risk for ethics review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines used for the governance review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eApplications triaged low/medium/high risk for governance review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePre-pandemic (Jan 2014- Dec 2019)\u003c/p\u003e\n \u003cp\u003eDuring or after pandemic (Jan 2020-Dec 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors influencing time to site activation (days) for 150 sites (including 19 imputed) initiations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariable p-value*\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMultivariable p-value**\u003c/em\u003e\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\u003eOverall\u003c/strong\u003e (n\u0026thinsp;=\u0026thinsp;150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e234 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e160 (114, 269)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e192 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250 (106, 617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e289 (85, 350)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease area of trial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e230 (74, 617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e249 (94, 346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrinology (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e244 (106, 372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurology (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e294 (82, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaediatrics (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e226 (138, 519)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia (n\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e236 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorea (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e230 (211, 312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e395 (262, 426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e174 (154, 252)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Zealand (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e184 (177, 196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial site ownership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e248 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e188 (114, 484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN/A (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e230 (154, 426)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutual acceptance of other ethics committee approvals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e230 (82, 601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e236 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e248 (94, 346)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriage by low, medium, or high risk for ethics review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e250 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e232 (138, 484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e150 (94, 224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriage by low, medium, or high risk for governance review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e236 (74, 617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e224 (94, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines for the ethics review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e233 (74, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e235 (144, 484)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e178 (114, 252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines for the governance review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e234 (74, 617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e247 (114, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;0.3**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-pandemic (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e248 (82, 657)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring or after pandemic (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e192 (74, 617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e* p-value from univariable comparison\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e** p-value from multivariable comparison that included disease area; phase; ownership status (N/A data removed from ownership status); scope guidelines by the ethics committee; mutual acceptance of other ethics committee findings; an ethics process that triaged applications as low, medium, or high risk; the use of scope guidelines by the governance body; a governance approval process that triaged applications as low, medium or high risk; and pre- or post-COVID in the regression model.\u003c/p\u003e\n\u003cp\u003eNA\u0026thinsp;=\u0026thinsp;not applicable because only one country (Australia) had data to enable multivariable analyses.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors influencing median time from ethics submission to ethics approval (days) for 150 site activations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariable p-value*\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMultivariable p-value**\u003c/em\u003e\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\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (5, 106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (16, 139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (2, 49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease area of trial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal (n\u0026thinsp;=\u0026thinsp;54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (2, 139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (3, 106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrinology (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97 (0, 203)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurology (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (16, 99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaediatrics (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96 (20, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia (n\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorea (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (11, 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (35, 105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (32, 32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Zealand (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (63, 63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial site ownership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;0.9*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (8, 215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (11, 105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines for ethics review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 (41, 215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutual acceptance of other ethics committee approvals\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (13, 215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (6, 125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriage by low, medium, or high risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (3, 215)\u003c/p\u003e\n \u003cp\u003e31 (15, 99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-pandemic (n\u0026thinsp;=\u0026thinsp;114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (0, 369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring or after pandemic (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (2, 139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e* p-value from univariable comparison\u003c/p\u003e\n\u003cp\u003e** p-value from multivariable comparison that included phase; disease area; country; trial site ownership status (N/A data removed from ownership status); scope guidelines by the ethics committee; mutual acceptance of other ethics committee findings; an ethics process that triaged applications as low, medium, or high risk; and pre- or post-COVID in the regression model.\u003c/p\u003e\n\u003cp\u003eNA\u0026thinsp;=\u0026thinsp;not applicable because only one country (Australia) had data to enable multivariable analyses.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactors influencing time to governance submission to governance approval (days) for 145 site activations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnivariable p-value*\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMultivariable p-value**\u003c/em\u003e\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\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (0, 89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (1, 140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (12, 99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease area of trial\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOncology (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (0, 112)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndocrinology (n\u0026thinsp;=\u0026thinsp;24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (3, 192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeurology (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (1, 140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePaediatrics (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (21, 433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAustralia (n\u0026thinsp;=\u0026thinsp;126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKorea (n\u0026thinsp;=\u0026thinsp;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (0, 102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHong Kong (n\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (9, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaiwan (n\u0026thinsp;=\u0026thinsp;0)\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\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Zealand (n\u0026thinsp;=\u0026thinsp;4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (7, 56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial site ownership\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic (n\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (3, 77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (0, 102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eScope guidelines used for the governance review\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (3, 135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriage by low, medium, or high risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes (n\u0026thinsp;=\u0026thinsp;111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (0, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81 (29, 135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.043**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown (n\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (6, 53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOVID-19 pandemic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-pandemic (n\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (0, 192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDuring or after pandemic (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (7, 489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e* p-value from univariable comparison\u003c/p\u003e\n\u003cp\u003e** p-value from multivariable comparison that included disease area; phase; trial site ownership status (N/A data removed from ownership status); the use of scope guidelines by the governance body; a governance approval process that triaged applications as low, medium, or high risk; and pre- or post-COVID in the regression model.\u003c/p\u003e\n\u003cp\u003eNA\u0026thinsp;=\u0026thinsp;not applicable because only one country (Australia) had data to enable multivariable analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project received ethics approval from the UNSW Sydney Human Research Ethics Committee on the 29\u003csup\u003eth\u003c/sup\u003e of March 2021.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot required for this publication\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not attract any external funding or grants.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSC, AM, LH, SJ, RH \u0026amp; BN made substantial contributions to the conception and design of the work. SC and AM acquired and processed the data, LH, primarily interpreted the data assisted by BN and SC. BN, SC, SJ, RH drafted and substantially revised the work. SC as corresponding author liaised with each author ensuring their input and revisions were accounted for.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLyndal Hones, George Clinical, Sydney, Australia\u003c/p\u003e\n\u003cp\u003eLuc Cambon, George Clinical, Sydney, Australia\u003c/p\u003e\n\u003cp\u003eMartin Febbo-Dobson, George Clinical, Sydney, Australia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJames Chung, George Clinical, Sydney, Australia\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNHMRC (National Health and Medical Research Council), Australian Clinical Trials, (2021) \u003cu\u003ehttps://www.australianclinicaltrials.gov.au/what-clinical-trial\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eDOH (Department of Health), (2021) Clinical trials, \u003cu\u003ehttps://www1.health.gov.au/internet/main/publishing.nsf/Content/Clinical-Trials\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eRothman DJ. Ethics and human experimentation. N Engl J Med1987;317:1195-1199\u003c/li\u003e\n\u003cli\u003eJunod, S.W., (2019) FDA and Clinical Drug Trials: A Short History, US Food and Drug Administration, Maryland \u003cu\u003ehttps://www.fda.gov/media/110437/download\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eWhitney SN, Schneider CE (2011) A method to estimate the cost in lives of ethics board review of biomedical research. J Int Med 269(4):396\u0026ndash;402\u003c/li\u003e\n\u003cli\u003eThompson SC, Sanfilippo FM, Briffa TG, Hobbs MS (2009) Towards better health research in Australia\u0026mdash;a plea to improve the efficiency of human research ethics committee processes. Med J Aust 190(11):652\u003c/li\u003e\n\u003cli\u003eJoffe, Steven. \u0026quot;Revolution or Reform in Human Subjects Research Oversight.\u0026quot; \u003cem\u003eJournal of Law, Medicine and Ethics,\u003c/em\u003e vol. 40, no. 4, Winter 2012, pp. 922-929. \u003cem\u003eHeinOnline\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eMcKeon S, Alexander E, Brodaty H, Ferris B, Frazer I, Little M. (2013) Strategic review of health and medical research in Australia\u0026ndash;better health through research. Canberra: Commonwealth of Australia, Department of Health and Ageing.\u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. \u003cu\u003ehttps://doi.org/10.1057/s41599-022-01269-3\u003c/u\u003e \u003c/li\u003e\n\u003cli\u003eSchulz KF, Altman DG, Moher D, for the CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials\u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. \u003cu\u003ehttps://doi.org/10.1057/s41599-022-01269-3\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eCrosby S, Rajadurai E, Jan S, Neal B, Holden R (2022) The effects on clinical trial activity of direct funding and taxation policy interventions made by government: A systematic review. PLOS ONE 17(9): e0269021. \u003cu\u003ehttps://doi.org/10.1371/journal.pone.0269021\u003c/u\u003e \u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. \u003cu\u003ehttps://doi.org/10.1057/s41599-022-01269-3\u003c/u\u003e \u003c/li\u003e\n\u003cli\u003eCare ACoSaQiH (2020) The National Clinical Trials Governance Framework Literature review. Care ACoSaQiH\u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. \u003cu\u003ehttps://doi.org/10.1057/s41599-022-01269-3\u003c/u\u003e \u003c/li\u003e\n\u003cli\u003eAgarwal R, Gaule P. (2022) What drives innovation? Lessons from COVID-19 R\u0026amp;D. Journal of Health Economics; 82. \u003cu\u003ehttps://doi.org/10.1016/j.jhealeco.2022.102591\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eCrosby, S., Rajadurai, E., Jan, S., Holden, R., Neal, R., (2022), The effects of government policies targeting ethics and governance processes on clinical trial activity and expenditure: a systematic review, Human and Social Sciences Communication, 266. \u003cu\u003ehttps://doi.org/10.1057/s41599-022-01269-3\u003c/u\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3047964/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3047964/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClinical trials are at the heart of medical research, enabling the development and implementation of new treatments. The time it takes to commence clinical trials at sites can be long, and ethics and governance approvals are key steps on the pathway to site activation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis paper explores factors influencing the times to ethics approval, governance approval and site activation. Broadly, these comprised trial characteristics (disease area and trial phase), site characteristics (public or private ownership, country) and characteristics of the ethics and governance processes (scope guidelines, mutual acceptance requirements and triage of projects by risk). Median times were compared between site initiations that were and were not exposed to each characteristic using non-parametric tests in univariable and multivariable regressions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere were data from 150 site activations done across 91sites, 16 trials and 5 countries. The overall median time to activation was 234 days (range 74 to 657), with ethics approval taking a median of 48 days (0 to 369) and governance approval a median of 34 days (0 to 489). Both the univariable and multivariable analyses identified associations of disease area, particularly oncology (p univariable\u0026thinsp;=\u0026thinsp;0.012, p multivariable\u0026thinsp;=\u0026thinsp;0.044), use of scope guidelines (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.020) and use of a triage process (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 0.043) with shorter median times for governance approval. These characteristics (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) plus early trial phase (p\u0026thinsp;=\u0026thinsp;0.028) were also predictive of shorter median times for ethics approval in univariable analyses, but none remained predictive in multivariable models (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.054). The only factors associated with reduced overall time to site activation in both univariable and multivariable analyses were early trial phase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;0.013) and mutual acceptance of ethics approvals (p\u0026thinsp;=\u0026thinsp;0.031, p\u0026thinsp;=\u0026thinsp;0.030).\u003c/p\u003e\u003ch2\u003eInterpretation\u003c/h2\u003e \u003cp\u003eTimes to ethics and governance approvals were only one third of total trial start-up time. Factors influencing times to approval and activation were somewhat inconsistent across analyses, but it seems likely that the introduction of selected governance and ethics processes can reduce approval times.\u003c/p\u003e","manuscriptTitle":"Factors influencing the time to ethics and governance approvals for clinical trials","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-03 15:32:23","doi":"10.21203/rs.3.rs-3047964/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-08-28T05:12:54+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-07-29T12:22:43+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-28T15:14:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-16T18:42:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trials","date":"2023-06-20T02:02:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ffec6e30-63d1-4dae-a0e5-4dd73aa2a28c","owner":[],"postedDate":"August 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-12-04T15:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-3047964","link":"https://doi.org/10.1186/s13063-023-07802-2","journal":{"identity":"trials","isVorOnly":false,"title":"Trials"},"publishedOn":"2023-12-01 15:00:52","publishedOnDateReadable":"December 1st, 2023"},"versionCreatedAt":"2023-08-03 15:32:23","video":"","vorDoi":"10.1186/s13063-023-07802-2","vorDoiUrl":"https://doi.org/10.1186/s13063-023-07802-2","workflowStages":[]},"version":"v1","identity":"rs-3047964","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3047964","identity":"rs-3047964","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}