Participant diversity and inclusive trial design: a meta-epidemiologic study of Canadian randomized clinical trials
Bibliographic record
Abstract
OBJECTIVES: To describe the demographic and social identities of participants in contemporary Canadian randomized clinical trials (RCTs). STUDY DESIGN AND SETTING: A meta-epidemiologic study included published reports of phase 2 and 3 RCTs that exclusively recruited adults living in Canada and were registered on ClinicalTrials.gov between January 1, 2010, and December 31, 2019. Study design and participant demographics were abstracted from eligible articles in duplicate using frameworks for understanding participant diversity such as PROGRESS-PLUS. RESULTS: We identified 118 RCTs with 17,387 participants. Most reported participant sex (n = 105, 89.0%), few reported gender (n = 12, 10.2%), and none reported both. Among articles reporting sex, there were 11,066 female (63.6%), 5402 male (32.8%), and one intersex (<0.1%) participants. There were 477 women (54.1%) and 404 men (45.9%) participants. No studies reported gender diverse participants. When excluding studies that only recruited one sex and/or gender, 51.8% of participants were male (n = 4774/9219) and 47.5% were men (n = 446/850). Race and/or ethnicity was reported for 4124 participants (23.7%) in 31 of 118 (26.3%) of RCTs; of these, 72.0% were White (n = 2969), 2.7% were Black (n = 113), and 0.2% were Indigenous (n = 7). Eligibility criteria related to specific PROGRESS-PLUS factors were rare except for cognition (n = 42, 35.6%), substance use (n = 25, 21.7%), pregnancy (n = 29, 24.5%), breastfeeding (n = 16, 13.6%), and older age (n = 26, 22.0%). CONCLUSION: The data are encouraging regarding representation of female and women participants in Canadian trials. Due to underreporting of other identities, we cannot identify additional groups who may be underrepresented. Work to improve reporting of race and/or ethnicity, among other identities, is needed. PLAIN LANGUAGE SUMMARY: Clinical trials tell us what drugs and procedures are helpful for patients. In certain specialties, like cancer and heart disease, clinical trials are made up mostly of men, White people, and younger people. This means that the results of these trials may be different for other groups of people, especially older people, women, and racialized people, who are more likely to have these diseases. We looked at the demographic identities of all participants in 118 Canadian clinical trials that were done between 2010 and 2019. Of the 17,387 participants, there were 11,066 female, 5402 male, 477 women, 404 men, and one intersex participant. We could find the race and/or ethnicity for only 4124 participants in 31 of the trials. Most participants (72.0%) were White, and only 2.7% were Black and 0.2% were Indigenous. These results tell us that reporting of identities in Canadian clinical trials is incomplete. Canadian clinical trialists should do a better job telling us who is in their trials. These results suggest that Canadian clinical trials are not representative of the general population, and that we need to explore the reasons that people are not participating in clinical trials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.292 | 0.512 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".