Evidence map›Paper›PMID 38737200›Full record

ArticleBMJ medicine2024

Participant characteristics and exclusion from phase 3/4 industry funded trials of chronic medical conditions: meta-analysis of individual participant level data.

Jennifer Lees, Jamie Crowther, Peter Hanlon, Elaine W Butterly, Sarah H Wild, Frances Mair, Bruce Guthrie, Katie Gillies, Sofia Dias, Nicky J Welton and 2 more

Abstract read
In one paragraph

Article in BMJ medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Jennifer LeesCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.ORCID 0000-0001-6331-0178
Jamie CrowtherCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.
Peter HanlonCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.
Elaine W ButterlyCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.ORCID 0000-0001-9410-0237
Sarah H WildCollege of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
Frances MairCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.
Bruce GuthrieCollege of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.ORCID 0000-0003-4191-4880
Katie GilliesHealth Services Research Unit, University of Aberdeen, Aberdeen, UK.
Sofia DiasCentre for Reviews and Dissemination, University of York, York, UK.
Nicky J WeltonPopulation Health Sciences, University of Bristol, Bristol, UK.
Srinivasa Vittal KatikireddiCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.ORCID 0000-0001-6593-9092
David A McAllisterCollege of Medical and Veterinary Life Sciences, University of Glasgow, Glasgow, UK.

Funding

Medical Research Council MC_UU_00022/2Wellcome Trust
6 · The paper itself

Abstract

Objectives: To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure). Design: Bayesian meta-analysis of individual participant level data. Setting: Industry funded phase 3/4 trials of chronic medical conditions. Participants: Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage. Main outcome measures: For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male Results: In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13). Conclusions: Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials. Trial registration number: PROSPERO CRD42018048202.

Indexed as

Clinical trialInternal medicineMedicineResearch designStatistics

Identifiers

PMID38737200
PMCPMC11085787

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Registered trials

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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.