Reporting and Representation of Race and Ethnicity in Clinical Trials of Pharmacotherapy for Mental Disorders
Bibliographic record
Abstract
Importance: Representation of race and ethnicity in randomized clinical trials (RCTs) is critical for understanding treatment efficacy across populations with different racial and ethnic backgrounds. Objective: To examine race and ethnicity representation and reporting across RCTs of pharmacotherapies for mental disorders. Data Sources: PubMed (Medline), Embase (Ovid), APA PsycInfo, and Web of Science were searched until March 1, 2024, to retrieve network meta-analyses including RCTs of pharmacotherapies for International Statistical Classification of Diseases and Related Health Problems, Tenth Revision mental disorders. Study Selection: RCTs that recruited people of any age with a diagnosis of a mental disorder and that tested the efficacy of any pharmacologic intervention vs any control arm. Data Extraction and Synthesis: Random-effects logit-transformed proportion meta-analyses were used to estimate prevalence rates of race and ethnicity groups and their temporal trends across RCTs and to compare US RCT prevalence rates with US Census data. The Preferred Reporting Items for Overviews of Reviews was used to report our review. Main Outcomes and Measures: Reporting of data and percentages of race and ethnicity. The year of publication, type of RCT, geographic location, age group, and sample size were also included. There were no deviations that occurred from the original protocol. Results: Data were obtained from 1683 RCTs (375 120 participants in total). Of these, 1363 (91.7% of participants) included participants aged 18 years or older; 680 RCTs (36.0% of participants) were from the US, 404 (17.1% of participants) were from Europe, and 293 (29.9% of participants) were from multiple geographic locations. Race and ethnicity were reported in 39.2% of RCTs; reporting was the highest in US-based RCTs (58.7%) and lowest in Central and South America (8.7%) and Asia and the Middle East (12.4%). Among participants, 2.7% (95% CI, 2.1%-3.5%) self-reported as Asian, 9.0% (95% CI, 8.1%-10.0%) as Black, 11.0% (95% CI, 9.1%-13.3%) as Hispanic among White, 80.2% (95% CI, 78.8%-81.5%) as White including Hispanic, and 5.8% (95% CI, 5.2%-6.4%) as other race or ethnicity, multiracial, or multiethnic. There was more frequent reporting of race and ethnicity in US RCTs (log odds increased by 0.066 each year) and less frequent reporting in non-US RCTs (log odds increased by 0.023 each year). Studies reporting race and ethnicity did not generally include larger sample sizes (mean sample size, 263.7 [95% CI, 15.0-860.3] participants) compared with those not reporting such data (mean sample size, 196.6 [95% CI, 12.0-601.3] participants), albeit not in all locations. In US RCTs, adults in the other or multiracial and multiethnic category were historically overrepresented, while adults in Asian, Black, Hispanic among White, and White including Hispanic categories were underrepresented; Asian, Black, and Hispanic among White children and adolescents are still currently underrepresented. Conclusions and Relevance: The findings of this meta-analysis suggest that differences in reporting race and ethnicity across geographic locations and underrepresentation of certain racial and ethnic groups in US-based RCTs highlight the need for international guidelines to ensure equitable recruitment and reporting 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 | 0.548 | 0.829 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.018 | 0.021 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".