Focus on sharing individual patient data distracts from other ways of improving trial transparency

Hoffmann, Tammy; Glasziou, Paul; Beller, Elaine; Goldacre, Ben; Chalmers, Iain

doi:10.1136/bmj.j2782

Cited by 16 publications

(11 citation statements)

References 28 publications

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“…Individual participant data may be useful for some meta‐analyses in systematic reviews, but IPD meta‐analyses are not always necessary to assess effectiveness and safety. There have been many discussions about the use of IPD (eg, Coady et al, Strom et al, and Taichman et al), yet the MUDS study and previous studies demonstrate that other data sources (eg, CSRs) may be equally or more useful for many systematic reviews . To reduce bias, and to reduce research waste, systematic reviewers should share the data sources used in their reviews.…”

Section: Resultsmentioning

confidence: 99%

Practical guidance for using multiple data sources in systematic reviews and meta‐analyses (with examples from the MUDS study)

Mayo‐Wilson

Fusco

et al. 2017

Research Synthesis Methods

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Data for individual trials included in systematic reviews may be available in multiple sources. For example, a single trial might be reported in 2 journal articles and 3 conference abstracts. Because of differences across sources, source selection can influence the results of systematic reviews. We used our experience in the Multiple Data Sources in Systematic Reviews (MUDS) study, and evidence from previous studies, to develop practical guidance for using multiple data sources in systematic reviews.We recommend the following: (1) Specify which sources you will use. Before beginning a systematic review, consider which sources are likely to contain the most useful data. Try to identify all relevant reports and to extract information from the most reliable sources. (2) Link individual trials with multiple sources. Write to authors to determine which sources are likely related to the same trials. Use a modified Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) flowchart to document both the selection of trials and the selection of sources. (3) Follow a prespecified protocol for extracting trial characteristics from multiple sources. Identify differences among sources, and contact study authors to resolve differences if possible. (4) Prespecify outcomes and results to examine in the review and meta‐analysis. In your protocol, describe how you will handle multiple outcomes within each domain of interest. Look for outcomes using all eligible sources. (5) Identify which data sources were included in the review. Consider whether the results might have been influenced by data sources used. (6) To reduce bias, and to reduce research waste, share the data used in your review.

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Section: Resultsmentioning

confidence: 99%

Practical guidance for using multiple data sources in systematic reviews and meta‐analyses (with examples from the MUDS study)

Mayo‐Wilson

Fusco

et al. 2017

Research Synthesis Methods

View full text Add to dashboard Cite

show abstract

“…Despite mandates by organisations, including the International Committee of Medical Journal Editors (ICMJE) requiring timely trial registration with an acceptable registry, [9] about half of trials are not registered [10]. Even for registered trials, it is uncommon for protocols or other related documents, to be provided as part of the registration process, with few trial registry systems requiring, checking, or even enabling upload of trial materials [11].…”

Section: Introductionmentioning

confidence: 99%

Analysis of articles directly related to randomized trials finds poor protocol availability and inconsistent linking of articles

Sender

Clark

Hoffmann

2020

Journal of Clinical Epidemiology

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Interpreting a randomised trial requires access to more than the main results paper. We aimed to determine the: 1) proportion of trials referring to the protocol in the trial report and their accessibility; 2) proportion of protocols accessible from trial registry entry and by trial registration number search; and 3) types of additional publications associated with trial reports. Study Design and Setting:A previously-gathered sample of randomised trials of nonpharmacological interventions published in 2009 was used. Trial reports and registry entries were searched for protocol mentions and obtained when possible. Related publications were identified using citation searching.Results: Of 133 trials, 96 (72%) mentioned the protocol within the report, 61 (64%) contained details about protocol acquisition, with 48 (36%) protocols obtainable. Of the 129 registered trials, 32 (25%) had protocols obtainable from registry entry. Citation tracking identified 1030 related publications; secondary analyses and qualitative studies most common. Conclusion:Trial protocols facilitate good trial conduct and interpretation. However, they are often not linked to the main report or registry and can be difficult to obtain. Many trials have related publications which are inconsistently linked. Trial registries and registration numbers could facilitate the threading of articles related to a trial, but currently do not.

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“…Principal investigators were worried about potential misleading secondary analyses or misinterpretation of the data, or data not being appropriate for the purpose of the secondary analyses. On a trial level, data misinterpretation could be prevented by providing associated supporting documents which contain meta‐data and ensure clarity of data elements for other investigators 39 . In the current study, only one‐third (34%) of principal investigators committed to sharing one or more of such supporting documents.…”

Section: Discussionmentioning

confidence: 88%

Data sharing—trialists' plans at registration, attitudes, barriers and facilitators: A cohort study and cross‐sectional survey

Tan

Askie

Hunter

et al. 2021

Research Synthesis Methods

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Data unavailability impedes research transparency and is a major problem for individual participant data (IPD) meta‐analyses as it reduces statistical power, increases risk of bias, and may even preclude completion. The primary objectives of this study were to determine IPD sharing plans reported in recently registered clinical trial registration records, how data sharing commitment relates to clinical trial characteristics, and principal investigators' attitudes, motivations and barriers to data sharing. The secondary objective was to derive recommendations to overcome identified barriers to data sharing. This was a retrospective cohort study of all interventional trials registered on the Australian New Zealand Clinical Trials Registry (ANZCTR) from 1 December 2018 to 30 November 2019, and an online cross‐sectional survey of their principal investigators. In the cohort study of all clinical trials registered on the ANZCTR in the study period (n = 1517), commitment to share data was low (22%, 329/1517). In the cross‐sectional survey (n = 281, 23% response rate), principal investigators showed strong support for the concept of data sharing (77%, 216/281) but a substantially lower intention to actually share data from their clinical trials (40%, 111/281). Major barriers to data sharing included lacking informed consent to share data, protecting participant confidentiality and preventing misinterpretation of data or misleading secondary analyses. There is a gap between high in‐principle support for data sharing, and low in‐practice intention from investigators to share data from their own clinical trials. Multiple pathways exist to bridge this gap by addressing the identified barriers to data sharing.

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Focus on sharing individual patient data distracts from other ways of improving trial transparency

Abstract: The debate on sharing clinical trial data has been dominated by individual patient data. Tammy Hoffmann and colleagues argue that, although patient level data are important, a focus on the other simpler elements of trial transparency should be the first priority

Cited by 16 publications

References 28 publications

Practical guidance for using multiple data sources in systematic reviews and meta‐analyses (with examples from the MUDS study)

Practical guidance for using multiple data sources in systematic reviews and meta‐analyses (with examples from the MUDS study)

Analysis of articles directly related to randomized trials finds poor protocol availability and inconsistent linking of articles

Data sharing—trialists' plans at registration, attitudes, barriers and facilitators: A cohort study and cross‐sectional survey

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