ABSTRACT Objectives To assess the availability of key clinical trial registration data and compliance with legal reporting requirements for all Phase 2-4 drug trials registered on the new European Clinical Trial Information System (CTIS) registry. This study is the first ever assessment of data quality and legal compliance with reporting requirements on CTIS. Design Cross-sectional observational study of CTIS registry data combined with manual review of results documents. Setting Cohort of all 7,547 Phase II-IV clinical trials registered on CTIS as of November 2025. Main outcome measures Number and proportion of missing data points in CTIS registration data. Proportion of completed clinical trials that are compliant with regulatory reporting requirements. Results Trial registration data quality was high overall with more than 99% of expected data present. Of 234 clinical trials legally required to report results, fewer than half (49.6%) fully reported results within the required timeframe, 20 trials (8.5%) fully reported results late, and 98 trials (41.9%) failed to fully report results. Legal compliance was similar for adult trials (79/158) and paediatric trials (37/76). Conclusions Sponsor compliance with legal reporting requirements is weak. Current efforts by European regulators to monitor and enforce compliance appear to be insufficient. New results reporting functions currently being set up by trial registries worldwide will require quality assurance processes. Trial registration Study protocol prospectively registered on OSF: https://osf.io/sn4j2/overview

Debates and policy initiatives addressing research reproducibility have expanded considerably in recent years. Yet, many of these measures remain generic and risk overlooking the lived realities of research practice. This study aims to explore researchers' perspectives on the barriers, facilitators, and motivators that shape reproducible research across diverse fields and career stages, using qualitative methods. Semi-structured interviews were conducted with 60 researchers affiliated with universities and research institutions across the European Union and the United Kingdom. Participants were sampled to ensure diversity in discipline, career stage, gender, and geography. The interviews explored experiences with barriers and facilitators for reproducible research, and the data were analyzed using framework analysis with a hybrid inductive-deductive approach. Five interrelated themes described barriers and facilitators influencing reproducibility: navigating the research ecosystem (incentives and policies of institutions, journals, and funders); social and cultural dynamics as drivers and barriers (disciplinary norms, generational differences, competition, and collaboration); resourcing reproducibility (skills, infrastructure, guidelines and standards, time, funding, and awareness); inside the research process (field-specific constraints, methodological transparency, research material sharing, and external restrictions); and personal commitment to shared responsibility (reflective motivations, pragmatic drivers, and perceptions of accountability). Researchers described reproducibility as less of an individual choice but as a socially and institutionally mediated activity, dependent on enabling conditions such as supportive policies, adequate infrastructure, and equitable resource distribution. Reproducibility reform cannot rely solely on individual researcher commitment or one-size-fits-all policies. Effective interventions must account for disciplinary and methodological diversity, provide targeted resources and training, and realign incentive structures to reward transparency and rigour. These findings highlight reproducibility as a collective responsibility across the research ecosystem, requiring coordinated action by researchers, institutions, funders, and publishers. Promoting reproducible practices in this systemic, context-sensitive manner is essential for fostering a more credible, equitable, and sustainable scientific enterprise.

OBJECTIVES:To determine whether an open science checklist can be useful for predicting the reproducibility of publications resulting from these grant proposals when used by grant referees assessing them. STUDY DESIGN AND SETTING:This is a comparative accuracy study design using funded grant proposals obtained from online sources (i.e., Open Grants, RIO Journal, NIH, and Grantome). Two independent groups of mock referees assessed open science practices in the proposals and predicted whether the resulting publications would be reproducible, with one group using an open science checklist as an intervention, and the other without. Then we attempted to reproduce the primary findings of a resulting publication from the grant proposal. Sensitivity, specificity, predictive values, and overall accuracy were calculated from 2 × 2 tables, comparing predicted vs. actual reproducibility. The primary outcome is the level of reproducibility, measured by the predictive value, the proportion of (non)reproducible study findings that are accurately predicted. This study was conducted between April and September 2025. RESULTS:Of seven out of 101 publications (6.9%, 95% CI: 2.8%-13.8%), the primary results could be reproduced. When using the checklist, only 16.8% of the proposals were expected to be reproducible, whereas without the checklist 75.2% were expected to be reproducible. When using the checklist, 17 proposals were expected to be reproducible, whereas only 2 out of these 17 could actually be reproduced (positive predictive value (PPV) 11.8% (95% CI: 3.3%-34.3%). Without using the checklist, 76 proposals were thought to be reproducible, whereas only six out of 76 could actually be reproduced (PPV 7.9% [95% CI: 3.7-16.2%]). Sensitivity analysis by research field was not conducted because of small sample sizes in most categories. CONCLUSION:The open science checklist has a low positive predictive value, as expected given the low reproducibility prevalence in our sample. Although the differences between the group using the checklist and the group that did not use the checklist may also have been caused by their level of knowledge of reproducibility and open science, neither group could predict which proposals would or would not be reproducible.

Computational reproducibility - the ability to recreate a study's statistical results from its original data and code - is rightfully considered a cornerstone of rigorous science. However, an analysis can be perfectly computationally reproducible while implementing something other than what the manuscript describes: for instance, a paper might wrongly describe which statistical model was used, how variables were specified in that model, or what parameter choices were selected. We introduce the concept of descriptive reproducibility: the degree to which narrative descriptions of analyses in manuscripts correspond to their implementation in analysis scripts. Where computational reproducibility asks whether the reported results follow from the code and data, descriptive reproducibility asks whether the code follows from the reported methods. We distinguish this concept from computational and methods reproducibility and present an indicative seven-dimension taxonomy of descriptive reproducibility. We also provide a worked example of how to conduct a descriptive reproducibility check, and explore why this concept may have received less attention to date: namely, that such checks are particularly difficult and time-demanding to conduct. We end by exploring solutions to this: namely, by unpacking the necessary ingredients required for an automated workflow to assist with descriptive reproducibility checks, and describing one potential such implementation: the CodeBot tool. We conclude that conducting descriptive and computational reproducibility checks in parallel can establish more comprehensive analytic transparency than either check alone.

Importance: Defining outcomes completely in trial registrations limits opportunities for reporting bias. That is, an incomplete outcome definition might be compatible with multiple results, so investigators could conduct many analyses and calculate different effects that would all appear to be consistent with the planned outcome. Objective: To evaluate outcome definitions for registered randomized controlled trials (RCTs) to determine whether they were complete or incomplete. Design: A cross-sectional study of RCTs registered on ClinicalTrials.gov and published in International Committee of Medical Journal Editors (ICMJE) member journals from 1 January to 30 June, 2025. Setting: ClinicalTrials.gov and RCTs published in ICMJE member journals. Participants: Outcomes registered before or soon after participant enrollment began for eligible RCTs. Exposure: This study characterized outcome registration completeness without a formal exposure or intervention. Main Outcomes and Measures: The proportion of outcomes that were defined completely (i.e., included a specific measurement, specific metric, variable type, time point, and cutoff, if applicable). Results were explored by outcome priority (i.e., Primary, Secondary, Other outcomes), trial regulatory status for RCTs governed by the US Food and Drug Administration Amendments Act (i.e., “Applicable Clinical Trials” versus other RCTs), by funder (i.e., Industry, National Institutes of Health, Other governmental organizations, Other), and journal. Results: Among 210 RCTs including 2543 registered outcomes, only 6 RCTs (2.9%) defined all outcomes completely and 326 outcomes (12.8%) were defined completely. Most outcomes defined the specific measurement (72.7%) and metric (85.7%), and the cutoff where applicable (65.0%) but not variable type (22.4%). In exploratory analyses, outcome definitions were more complete for Primary outcomes compared with Secondary and Other outcomes (20.5% versus 12.2% and 9.2%, respectively). Outcomes in Applicable Clinical Trials were better defined than outcomes in other RCTs (15.5% versus 11.0%), and outcomes in RCTs sponsored by Industry or funded by the National Institutes of Health were better defined than outcomes in Other RCTs (17.0% and 19.6% versus 7.4%, respectively). There was no evidence of differences across journals. Conclusions and Relevance: Most RCTs published in ICMJE member journals did not define their outcomes completely in registrations. Complete outcome definitions in trial registrations could prevent reporting biases.

Defining outcomes completely before conducting clinical trials helps to mitigate reporting biases; however, there is limited guidance to help investigators define outcomes completely. We aimed to develop a structured approach for defining trial outcomes completely and consistently. We reviewed literature, developed preliminary rules for defining outcomes, and refined them iteratively. We randomly selected randomized controlled trials (RCTs) on ClinicalTrials.gov that registered before their start dates and posted results by January 4, 2024. The 225 included RCTs evaluated 3,424 outcomes. Two raters independently applied preliminary rules to define each outcome. When raters encountered outcomes they could not define, we refined the rules. We continued this process until no further changes were needed. We discussed and finalized our approach in a consensus meeting. We define an “outcome” as a value for each participant that will be used in analysis to generate study results. A complete outcome definition includes six elements: outcome domain, specific measurement, specific metric, cutoff, variable type, and timepoint. We developed rules for naming specific measurements for both subjective and objective outcomes. We expanded on prior work by developing more comprehensive categories for specific metrics. We introduced "cutoff" as a distinct element with three subelements. To clarify the boundary between outcome definitions and statistical methods, we replaced a previously described element, "method of aggregation," with "variable type," which refers to whether the value for each individual is continuous or categorical. Trialists and sponsors could use this approach alongside other guidelines to define outcomes in trial registrations, protocols, and result reports.

Abstract Academics, policymakers and other research stakeholders have broadly discussed strategies to improve research reproducibility, with heightened attention given to open science practices. One strand of these discussions adopts a behavioural perspective centred on researchers. This narrative review applied the behaviour change wheel (BCW) framework to analyse 103 interventions evaluated for their effectiveness in improving reproducibility and reproducibility-related practices. We systematically mapped the included interventions across the nine BCW intervention types and seven supporting policy categories, highlighting which mechanisms have been most and least frequently evaluated empirically. We then interpreted these mechanisms using the COM-B behaviour model (capability–opportunity–motivation). The analysis revealed that ‘coercion’ and ‘persuasion’ were the most commonly studied types of intervention, implemented through ‘regulations’ and ‘guidelines’, with active enforcement appearing to be more effective than voluntary adherence. By contrast, ‘incentivisation’, ‘training’ and ‘modelling'—central to many reform agendas—were rarely evaluated empirically, and ‘communication’ and ‘legislation’ seldom appeared as implementation policies. Furthermore, most interventions were journal-led rather than funder- or university-led, indicating the limited involvement of other important stakeholders. Our analysis suggests that the key to supporting reproducible and transparent research lies in a strategic and integrated approach that targets all components of COM-B, with accountability mechanisms, context-sensitive measures and synergistic action involving multiple stakeholders.

BACKGROUND:Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find them. Summary results of clinical trials are often not published in trial registries, and publications containing trial results are often not explicitly linked to their respective trial registrations. Finding these results is important to researchers, systematic reviewers, research funders, regulators, clinical practitioners, and patients. METHODS:We developed TrialScout, a computer program that uses a large language model to match clinical trials registered on ClinicalTrials.gov with corresponding result publications indexed in PubMed. TrialScout's performance was evaluated through comparison to human-coded matches from previous studies of results reporting rates. Subsequently, TrialScout was applied in a cross-sectional analysis of a random sample of 9,600 completed or terminated trials. RESULTS:TrialScout had a sensitivity of 92.5% and a specificity of 81.2% compared to human coders. Manual review of 200 cases where TrialScout disagreed with human researchers showed that a majority (123/200, 61.5%, 95% CI, 54.4-68.3%) of disagreements were due to human errors. When used on 9,600 sampled trials in ClinicalTrials.gov, TrialScout found result publications for 6,110 (63.6%) of trials. DISCUSSION:TrialScout reliably located results of completed clinical trials. The tool offers benefits in terms of speed and efficiency. Estimating TrialScout's accuracy is limited by the lack of a true gold standard. TrialScout can accelerate the process of locating trial results in the scientific literature and can assist in monitoring trial reporting practices.

OBJECTIVES:This study aimed to map the presence, public availability, and content of clinical trial data sharing policies, data management and sharing plans, and data use agreements among the most prolific public and private clinical trial sponsors operating in Europe. STUDY DESIGN AND SETTING:We included organization-level documents describing approaches to clinical trial data sharing or data management from the top 20 public and top 20 private sponsors ranked by the number of trials registered in the European Union (EU) Clinical Trials Information System. Eligible materials comprised publicly available or sponsor-shared policies, guidelines, statements, templates, and agreements relevant to clinical trial data sharing or management. Evidence was identified through systematic searches of sponsors' public websites, structured Google searches, and major data management plan platforms, complemented by direct contact with sponsors to verify findings and request missing documentation. All sources were archived and cataloged. Two reviewers independently extracted data using a structured form, capturing the existence, accessibility, and content of data sharing policies, data management and sharing plans, and data use agreements. Quantitative data were summarized descriptively, and a noninterpretive descriptive content analysis was conducted to characterize recurring policy elements and areas of heterogeneity. RESULTS:Among 40 sponsors, private sponsors were substantially more likely than public sponsors to make trial-specific data sharing policies and data use agreements publicly accessible, often via established data sharing platforms. Public sponsors more frequently referenced data management and sharing plans, but these were heterogeneous in scope and often embedded within broader institutional governance documents rather than tailored to clinical trials. Across sectors, General Data Protection Regulation compliance, data protection, and legal safeguards were emphasized, while operational aspects such as dataset readiness, review criteria, and downstream responsibilities varied widely. Overall response rate to sponsor verification was 37.5%. CONCLUSION:Clinical trial data sharing governance in the EU shows a marked sectoral imbalance among the top sponsors. Private sponsors tend to provide more detailed and operationally explicit documentation, whereas public sponsors often articulate high-level commitments without trial-specific guidance. Greater clarity and standardization, particularly among public sponsors, could improve transparency and facilitate responsible data reuse, while remaining compatible with General Data Protection Regulation requirements.

Importance:Open science practices are essential for improving transparency, reproducibility, and trust in biomedical research. Journals play a critical role in promoting these practices through editorial policies, yet implementation and impact remain unclear. Objective:To evaluate the open science policies of leading medical journals and assess implementation and detectability of practices using automated tools. Design, Setting, and Participants:This cross-sectional study of journal policies and open science practices evaluated research articles published in 10 leading general medical journals from January 2020 to December 2023. Additionally, the diagnostic accuracy of automated tools was validated against manual extraction. Exposures:Journal policies regarding open science practices and article-level implementation of 13 core practices including registration, protocol sharing, and intention to share data. Main Outcomes and Measures:Journal policies were assessed using the Transparency and Openness Promotion guidelines (TOP2025). At the article level, 13 core open science practices were examined. Additionally, 9 validated automated tools were applied to detect these practices, and their performance was compared with manual extraction of articles. Results:Overall, 15 624 research articles published in 10 general medical journals were analyzed (validation subset, 312 articles: 103 randomized clinical trials [RCTs], 98 meta-analyses, and 111 with other designs). At the journal level, TOP2025 evaluation identified substantial heterogeneity in policies, primarily applied to clinical trials. At the article level, open science practices were more frequently implemented in RCTs than other designs: registration (RCTs: 99% [95% CI, 97%-100%]; meta-analyses: 69% [95% CI, 56%-79%]; other designs: 16% [95% CI, 9%-26%]), protocol sharing (RCTs: 96% [95% CI, 93%-98%]; meta-analyses: 67% [95% CI, 54%-78%]; other designs: 20% [95% CI, 12%-33%]), and intention to share data (RCTs: 79% [95% CI, 67%-87%]; meta-analyses: 65% [51%-77%]; other designs: 70% [95% CI, 57%-81%]). Automated tools showed variable performance (F1 scores, 0.06-1.00) and generally underestimated practices. Conclusions and Relevance:In this cross-sectional study of 15 624 articles in 10 leading medical journals, journal policies were only partially aligned with TOP2025, and article-level open science practices were more frequently reported for RCTs than for other designs, supporting the need for stronger journal policies.

OBJECTIVES:To systematically evaluate timely reporting of clinical trial results at medical universities and university hospitals in the Nordic countries. STUDY DESIGN AND SETTING:In this cross-sectional study, we included trials (regardless of intervention) registered in the European Union (EU) Clinical Trials Registry and/or ClinicalTrials.gov, completed 2016-2019 and led by a university with medical faculty or university hospital in Denmark, Finland, Iceland, Norway, or Sweden. We identified summary results posted at the trial registries and conducted systematic manual searches for results publications (eg, journal articles, preprints). We present proportions with 95% confidence intervals (CI) and medians with interquartile range (IQR). PROTOCOL:https://osf.io/wua3r. RESULTS:Among 2112 included clinical trials, 1650 (78.1%, 95% CI 76.3%-79.8%) reported any results during our follow-up; 1097 (51.9%, 95% CI 49.8%-54.1%) reported any results within 2 years of the global completion date; and 48 (2.3%, 95% CI 1.7%-3.0%) posted summary results in the registry within 1 year. The median time from global completion date to results reporting was 690 days (IQR 1103). 856/1681 (50.9%) of ClinicalTrials.gov registrations were prospective. Denmark contributed approximately half of all trials. Reporting performance varied widely between institutions. CONCLUSION:Missing and delayed results reporting of academically led clinical trials are a pervasive problem in the Nordic countries. We relied on trial registry information, which can be incomplete. Institutions, funders, and policymakers need to support trial teams, ensure regulation adherence, and secure trial reporting before results are permanently lost. PLAIN LANGUAGE SUMMARY:Reporting of results from clinical trials is necessary for evidence-based clinical decision-making. We followed up reporting of clinical trials in the Nordic countries sponsored by medical universities and university hospitals. Of 2112 studies completed 2016-2019 in two major trials registries, about half reported results in any form within 24 months, and more than one in five did not report results at all. These results show that there is a need for improvement in the reporting of Nordic clinical trials.

Background:Many interventions, especially those linked to open science, have been proposed to improve reproducibility in science. To what extent these propositions are based on scientific evidence from empirical evaluations is not clear. Aims:The primary objective is to identify Open Science interventions that have been formally investigated regarding their influence on reproducibility and replicability. A secondary objective is to list any facilitators or barriers reported and to identify gaps in the evidence. Methods:We will search broadly by using electronic bibliographic databases, broad internet search, and contacting experts in the field of reproducibility, replicability, and open science. Any study investigating interventions for their influence on the reproducibility and replicability of research will be selected, including those studies additionally investigating drivers and barriers to the implementation and effectiveness of interventions. Studies will first be selected by title and abstract (if available) and then by reading the full text by at least two independent reviewers. We will analyze existing scientific evidence using scoping review and evidence gap mapping methodologies. Results:The results will be presented in interactive evidence maps, summarized in a narrative synthesis, and serve as input for subsequent research. Review registration:This protocol has been pre-registered on OSF under doi https://doi.org/10.17605/OSF.IO/D65YS.

Background Many interventions, especially those linked to open science, have been proposed to improve reproducibility in science. To what extent these propositions are based on scientific evidence from empirical evaluations is not clear. Aims The primary objective is to identify Open Science interventions that have been formally investigated regarding their influence on reproducibility and replicability. A secondary objective is to list any facilitators or barriers reported and to identify gaps in the evidence. Methods We will search broadly by using electronic bibliographic databases, broad internet search, and contacting experts in the field of reproducibility, replicability, and open science. Any study investigating interventions for their influence on the reproducibility and replicability of research will be selected, including those studies additionally investigating drivers and barriers to the implementation and effectiveness of interventions. Studies will first be selected by title and abstract (if available) and then by reading the full text by at least two independent reviewers. We will analyze existing scientific evidence using scoping review and evidence gap mapping methodologies. Results The results will be presented in interactive evidence maps, summarized in a narrative synthesis, and serve as input for subsequent research. Review registration This protocol has been pre-registered on OSF under doi https://doi.org/10.17605/OSF.IO/D65YS

Objectives: Since 2017, the UK government has made concerted efforts to ensure the dissemination of clinical trials conducted at public research institutions. This study aims to understand how stakeholders within these institutions responded to these pressures and modified internal policies and processes while identifying best practices and barriers to improved transparency practice. Methods: Research governance and trial management staff from UK public research institutions (i.e., Universities and NHS Trusts) in England, Scotland and Wales participated in semi -structured interviews. Interviews were analysed using thematic analysis, aided by the framework method. Results: Between November 2020 and July 2021, 14 individual participants were recruited from 11 different institutions. They worked in research governance, administration, and management. Almost universally, new policies and procedures have been established to ensure investigators are aware of, and supported in, fulfilling their transparency commitments, however challenges remain. Trials of medicinal products, as the most closely regulated research, consequently received the most attention. National professional networks aid in sharing knowledge and best practice within this community. Conclusions: Investment in the institutional governance of transparency is essential to achieving optimal transparency practices. Universities and hospitals share responsibility for ensuring research is performed and reported to regulatory standards. Facing political pressure, public research institutions in the UK have made efforts to improve their transparency practice which can provide key insights for similar efforts elsewhere.

Electronic health records (EHRs) and other administrative health data are increasingly used in research to generate evidence on the effectiveness, safety, and utilisation of medical products and services, and to inform public health guidance and policy. Reproducibility is a fundamental step for research credibility and promotes trust in evidence generated from EHRs. At present, ensuring research using EHRs is reproducible can be challenging for researchers. Research software platforms can provide technical solutions to enhance the reproducibility of research conducted using EHRs. In response to the COVID-19 pandemic, we developed the secure, transparent, analytic open-source software platform OpenSAFELY designed with reproducible research in mind. OpenSAFELY mitigates common barriers to reproducible research by: standardising key workflows around data preparation; removing barriers to code-sharing in secure analysis environments; enforcing public sharing of programming code and codelists; ensuring the same computational environment is used everywhere; integrating new and existing tools that encourage and enable the use of reproducible working practices; and providing an audit trail for all code that is run against the real data to increase transparency. This paper describes OpenSAFELY’s reproducibility-by-design approach in detail.

ObjectiveTo identify the availability of results for trials registered on the European Union Clinical Trials Register (EUCTR) compared with other dissemination routes to understand its value as a results repository.DesignCross sectional audit study.SettingEUCTR protocols and results sections, data extracted 1-3 December 2020.PopulationRandom sample of 500 trials registered on EUCTR with a completion date of more than two years from the beginning of searches (ie, 1 December 2018).Main outcome measuresProportion of trials with results across the examined dissemination routes (EUCTR, ClinicalTrials.gov, ISRCTN registry, and journal publications), and for each dissemination route individually. Prespecified secondary outcomes were number and proportion of unique results, and the timing of results, for each dissemination route.ResultsIn the sample of 500 trials, availability of results on EUCTR (53.2%, 95% confidence interval 48.8% to 57.6%) was similar to the peer reviewed literature (58.6%, 54.3% to 62.9%) and exceeded the proportion of results available on other registries with matched records. Among the 383 trials with any results, 55 (14.4%, 10.9% to 17.9%) were only available on EUCTR. Also, after the launch of the EUCTR results database, median time to results was fastest on EUCTR (1142 days, 95% confidence interval 812 to 1492), comparable with journal publications (1226 days, 1074 to 1551), and exceeding ClinicalTrials.gov (3321 days, 1653 to undefined). For 117 trials (23.4%, 19.7% to 27.1%), however, results were published elsewhere but not submitted to the EUCTR registry, and no results were located in any dissemination route for 117 trials (23.4%, 19.7% to 27.1).ConclusionsEUCTR should be considered in results searches for systematic reviews and can help researchers and the public to access the results of clinical trials, unavailable elsewhere, in a timely way. Reporting requirements, such as the EU's, can help in avoiding research waste by ensuring results are reported. The registry's true value, however, is unrealised because of inadequate compliance with EU guidelines, and problems with data quality that complicate the routine use of the registry. As the EU transitions to a new registry, continuing to emphasise the importance of EUCTR and the provision of timely and complete data is critical. For the future, EUCTR will still hold important information from the past two decades of clinical research in Europe. With increased efforts from sponsors and regulators, the registry can continue to grow as a source of results of clinical trials, many of which might be unavailable from other dissemination routes.

BACKGROUND:Researchers conducting trials have a responsibility to publish the results of their work in a peer-reviewed journal, and failure to do so may introduce bias that affects the accuracy of available evidence. Moreover, failure to publish results constitutes research waste. OBJECTIVES:To systematically review research reports that followed clinical trials from their inception and their investigated publication rates and time to publication. We also aimed to assess whether certain factors influenced publication and time to publication. SEARCH METHODS:We identified studies by searching MEDLINE, Embase, Epistemonikos, the Cochrane Methodology Register (CMR) and the database of the US Agency for Healthcare Research and Quality (AHRQ), from inception to 23 August 2023. We also checked reference lists of relevant studies and contacted experts in the field for any additional studies. SELECTION CRITERIA:Studies were eligible if they tracked the publication of a cohort of clinical trials and contained analyses of any aspect of the publication rate or time to publication of these trials. DATA COLLECTION AND ANALYSIS:Two review authors performed data extraction independently. We extracted data on the prevalence of publication and the time from the trial start date or completion date to publication. We also extracted data from the clinical trials included in the research reports, including country of the study's first author, area of health care, means by which the publication status of these trials were sought and the risk of bias in the trials. MAIN RESULTS:A total of 204 research reports tracking 165,135 trials met the inclusion criteria. Just over half (53%) of these trials were published in full. The median time to publication was approximately 4.8 years from the enrolment of the first trial participant and 2.1 years from the trial completion date. Trials with positive results (i.e. statistically significant results favouring the experimental arm) were more likely to be published than those with negative or null results (OR 2.69, 95% CI 2.02 to 3.60; 19 studies), and they were published in a shorter time (adjusted HR 1.92, 95% CI 1.51 to 2.45; 4 studies). On average, trials with positive results took 2 years to publish, whereas trials with negative or null results took 2.6 years. Large trials were more likely to be published than smaller ones (adjusted OR 1.92, 95% CI 1.33 to 2.77; 11 studies), and they were published in a shorter time (adjusted HR 1.41, 95% CI 1.18 to 1.68; 7 studies). Multicentre trials were more likely to be published than single-centre trials (adjusted OR 1.20, 95% CI 1.03 to 1.40; 2 studies). We found no difference between multicentre and single-centre trials in time to publication. Trials funded by non-industry sources (e.g.governments or universities) were more likely to be published than trials funded by industry (e.g. pharmaceutical companies or for-profit organisations) (adjusted OR 2.13, 95% CI 1.82 to 2.49; 14 studies); they were also published in a shorter time (adjusted HR 1.46, 95% CI 1.15 to 1.86; 7 studies). AUTHORS' CONCLUSIONS:Our updated review shows that trial publication is poor, with only half of all trials that are conducted being published. Factors that may make publication more likely and lead to faster publication are positive results, large sample size and being funded by non-industry sources. Differences in publication rates result in publication bias and time-lag bias that may influence findings and therefore ultimately affect treatment decisions. Systematic review authors should consider the possibility of time-lag bias when conducting a systematic review, especially when updating their review. FUNDING:This Cochrane review had no dedicated funding. REGISTRATION:This review combines and updates two earlier Cochrane reviews. The two protocols and previous versions of the two updated reviews are available via 10.1002/14651858.MR000006 and 10.1002/14651858.MR000006.pub3 and 10.1002/14651858.MR000011 and 10.1002/14651858.MR000011.pub2.