CoChat
Sign Up
Cited by 48Research Data Management PracticesMeta-analysis and systematic reviewsAcademic Publishing and Open Access

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

Open access
Tan, Aidan Christopher, Askie, Lisa M., Hunter, Kylie Elizabeth, Barba, Angie, Simes, Robert John, Seidler, Anna Lene·2021·Research Synthesis Methods
DOI 10.1002/jrsm.1500
Open this paper in CoChat — free account

Abstract

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.

Integrity signals

Registry

Crossref · 10.1002/jrsm.1500

Cites

47 of 47 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • One review of eight published meta-epidemiological studies reported that trials with a high risk of influence from industry funders reported 12% (odds ratio) more beneficial effects (95% CI 3% to 19%), on average, than comparable trials without high risk of funder influence.

    “One review of eight published meta-epidemiological studies reported that trials with a high risk of influence from industry funders, such as on trial design, conduct, and analysis, reported 12% (odds ratio) more beneficial effects (95% confidence interval (CI) 3% to 19%), on average, than comparable trials without high risk of funder influence.52”

    From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [52]

  • Industry funded trials with greater funder involvement tend to report more favourable results than comparable trials without such involvement.

    “Industry funded trials with greater funder involvement tend to report more favourable results (ie, larger estimates of intervention effects) than comparable trials without such involvement.”

    From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [52]

  • One review of eight published meta-epidemiological studies found that on average odds ratios from industry funded trials did not differ significantly from non-industry funded trials; however, in the subset of trials with a high risk of industry funder influence, the difference was 12% (95% CI 3% to 19%).

    “One review of eight published meta-epidemiological studies reported that, on average, odds ratios from industry funded trials did not differ significantly from those of non-industry funded trials. However, in the subset of trials with a high risk of industry funder influence (eg, on trial design, conduct, and analysis), the difference was 12% (95% CI 3% to 19%).52”

    From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [52]

  • Industry funded trials might report more favourable results than comparable trials funded by other sources, although the evidence is less certain.

    “Industry funded trials might also report more favourable results (ie, estimates of intervention effects) than comparable trials that are funded by other sources, although the evidence is less certain.”

    From SPIRIT 2025 explanation and elaboration: updated guideline for protocols of randomised trials (2025) · cites this paper as [52]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Related

Authors
Tan, A. C., Askie, L. M., Hunter, K. E., Barba, A., Simes, R. J., & Seidler, A. L.
Affiliations
The University of Sydney (AU)
Corresponding author
Seidler, Anna Lene
ORCID
Venue
Research Synthesis Methods
Publisher
Wiley
Published
2021-06-15
Volume
12
Issue
5
Pages
641-657
ISSN
  • 1759-2879 (ISSN-L)
  • 1759-2887
DOI
10.1002/jrsm.1500
OpenAlex
W3165880742
Cited by
48
References
47
Citations by year
2026: 7 · 2025: 11 · 2024: 15 · 2023: 7 · 2022: 7 · 2021: 1
FWCI
6.77
Type
journal article
Language
en
Open access
Licence
Locations
  • Research Synthesis Methods (publishedVersion)
  • PubMed (publishedVersion)
  • The Sydney eScholarship Repository (The University of Sydney) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
Keywords
data sharing, individual patient data, clinical trial registration, barriers to data sharing, data availability, participant confidentiality, informed consent, research transparency
MeSH terms
Attitude, Australia, Clinical Trials as Topic, Cross-Sectional Studies, Humans, Retrospective Studies, Cohort Studies, Information Dissemination

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review