Evidence map›Paper›PMID 27410240›Full record

ArticleBMC medical research methodology2016

Best practice for analysis of shared clinical trial data.

Sally Hollis, Christine Fletcher, Frances Lynn, Hans-Joerg Urban, Janice Branson, Hans-Ulrich Burger, Catrin Tudur Smith, Matthew R Sydes, Christoph Gerlinger

Abstract readLetter
In one paragraph

Article in BMC medical research methodology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

  1. Trial
  2. A Vector Theory of Assessing Clinical Trials: An Application to Bioequivalence.Journal of cardiovascular development and disease · 2024
    Article
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  4. Article
  5. Review
  6. Article
  7. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Sally HollisAstraZeneca, Alderley Park, Cheshire, SK10 4TG, UK.
Christine FletcherAmgen Ltd., 240 Cambridge Science Park, Cambridge, CB4 0WD, UK.
Frances LynnBioMarin, 10 Bloomsbury Way, London, WC1A 2SL, UK.
Hans-Joerg UrbanHoffman-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland.
Janice BransonNovartis Pharma AG, Basel, Switzerland.
Hans-Ulrich BurgerHoffman-La Roche, Grenzacherstrasse 124, 4070, Basel, Switzerland.
Catrin Tudur SmithMRC North West Hub for Trials Methodology Research, University of Liverpool, Liverpool, UK.
Matthew R SydesMRC Clinical Trials Unit at UCL, Institute of Clinical Trials & Methodology, Aviation House, 125 Kingsway, London, WC2B 6NH, UK.
Christoph GerlingerResearch and Development Statistics, Bayer Pharma AG, 13353, Berlin, Germany. christoph.gerlinger@bayer.com.

Funding

Medical Research Council MC_UU_12023/24
6 · The paper itself

Abstract

backgroundGreater transparency, including sharing of patient-level data for further research, is an increasingly important topic for organisations who sponsor, fund and conduct clinical trials. This is a major paradigm shift with the aim of maximising the value of patient-level data from clinical trials for the benefit of future patients and society. We consider the analysis of shared clinical trial data in three broad categories: (1) reanalysis - further investigation of the efficacy and safety of the randomized intervention, (2) meta-analysis, and (3) supplemental analysis for a research question that is not directly assessing the randomized intervention. DISCUSSION: In order to support appropriate interpretation and limit the risk of misleading findings, analysis of shared clinical trial data should have a pre-specified analysis plan. However, it is not generally possible to limit bias and control multiplicity to the extent that is possible in the original trial design, conduct and analysis, and this should be acknowledged and taken into account when interpreting results. We highlight a number of areas where specific considerations arise in planning, conducting, interpreting and reporting analyses of shared clinical trial data. A key issue is that that these analyses essentially share many of the limitations of any post hoc analyses beyond the original specified analyses. The use of individual patient data in meta-analysis can provide increased precision and reduce bias. Supplemental analyses are subject to many of the same issues that arise in broader epidemiological analyses. Specific discussion topics are addressed within each of these areas. Increased provision of patient-level data from industry and academic-led clinical trials for secondary research can benefit future patients and society. Responsible data sharing, including transparency of the research objectives, analysis plans and of the results will support appropriate interpretation and help to address the risk of misleading results and avoid unfounded health scares.

Indexed as

Clinical Trials as TopicInformation DisseminationDrug IndustryHumansMeta-Analysis as TopicQuality Assurance, Health Care

Identifiers

PMID27410240
PMCPMC4943488

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.