Evidence map›Paper›PMID 36936559›Full record

ArticleBMJ medicine2022

Effect of age, sex, and morbidity count on trial attrition: meta-analysis of individual participant level data from phase 3/4 industry funded clinical trials.

Jennifer S Lees, Peter Hanlon, Elaine W Butterly, Sarah H Wild, Frances S Mair, Rod S Taylor, Bruce Guthrie, Katie Gillies, Sofia Dias, Nicky J Welton and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMJ medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 4 pooled it
5.7field-weighted citation impact, top 4% of its field
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

17 citing papers in PubMed, 4 syntheses or guidelines pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
  17. Observational
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

11 authors at 5 institutions in 1 country.

Jennifer S LeesUniversity of Glasgow, Glasgow, UK.ORCID 0000-0001-6331-0178
Peter HanlonUniversity of Glasgow, Glasgow, UK.
Elaine W ButterlyInstitute of Health and Wellbeing, University of Glasgow, Glasgow, UK.ORCID 0000-0001-9410-0237
Sarah H WildUniversity of Edinburgh, Edinburgh, UK.
Frances S MairUniversity of Glasgow, Glasgow, UK.
Rod S TaylorUniversity of Glasgow, Glasgow, UK.
Bruce GuthrieUniversity of Edinburgh, Edinburgh, UK.ORCID 0000-0003-4191-4880
Katie GilliesUniversity of Aberdeen, Aberdeen, UK.
Sofia DiasUniversity of York, York, UK.
Nicky J WeltonPopulation Health Sciences, University of Bristol, Bristol, UK.
David A McAllisterUniversity of Glasgow, Glasgow, UK.
University of Glasgow · GBUniversity of Edinburgh · GBUniversity of Aberdeen · GBUniversity of Bristol · GBUniversity of York · GB

Funding

Chief Scientist Office PCL/20/10Medical Research Council MR/P015298/1Medical Research Council MR/S021949/1Wellcome Trust 201492/Z/16/Z
6 · The paper itself

Abstract

Objectives: To estimate the association between individual participant characteristics and attrition from randomised controlled trials. Design: Meta-analysis of individual participant level data (IPD). Data sources: Clinical trial repositories (Clinical Study Data Request and Yale University Open Data Access). Eligibility criteria for selecting studies: Eligible phase 3 or 4 trials identified according to prespecified criteria (PROSPERO CRD42018048202). Main outcome measures: Association between comorbidity count (identified using medical history or concomitant drug treatment data) and trial attrition (failure for any reason to complete the final trial visit), estimated in logistic regression models and adjusted for age and sex. Estimates were meta-analysed in bayesian linear models, with partial pooling across index conditions and drug classes. Results: In 92 trials across 20 index conditions and 17 drug classes, the mean comorbidity count ranged from 0.3 to 2.7. Neither age nor sex was clearly associated with attrition (odds ratio 1.04, 95% credible interval 0.98 to 1.11; and 0.99, 0.93 to 1.05, respectively). However, comorbidity count was associated with trial attrition (odds ratio per additional comorbidity 1.11, 95% credible interval 1.07 to 1.14). No evidence of non-linearity (assessed via a second order polynomial) was seen in the association between comorbidity count and trial attrition, with minimal variation across drug classes and index conditions. At a trial level, an increase in participant comorbidity count has a minor impact on attrition: for a notional trial with high level of attrition in individuals without comorbidity, doubling the mean comorbidity count from 1 to 2 translates to an increase in trial attrition from 29% to 31%. Conclusions: Increased comorbidity count, irrespective of age and sex, is associated with a modest increased odds of participant attrition. The benefit of increased generalisability of including participants with multimorbidity seems likely to outweigh the disadvantages of increased attrition.

Indexed as

clinical trialinternal medicinemedicineresearch design

Identifiers

PMID36936559
PMCPMC9978693
OpenAlexW4294053300

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.