Meta‐analysis of sex and racial subgroup participation rates and differential treatment effects for trials in solid tumor malignancies leading to US Food and Drug Administration registration between 2010 and 2021
Bibliographic record
Abstract
BACKGROUND: The lack of sociodemographic diversity in clinical trials limits the generalizability of results. The authors examined participation rates and effect modification by sex and race in oncology trials. METHODS: The authors extracted outcome data stratified by sex and race for registration trials supporting US Food and Drug Administration (FDA) approval (2010-2021). Effect modification by race and sex was examined using quantitative and qualitative methods. A random-effects meta-analysis and pairwise comparison of progression-free survival (PFS) and overall survival (OS) outcomes was conducted by sex and race. RESULTS: Ninety-five trials with 123 end points and 54,365 patients provided information on sex. Trial patients were more often male (n = 35,482; 65% vs. 56% male patients in US Surveillance, Epidemiology, and End Results [SEER] data), although the proportion of male patients was similar after adjusting by tumor type (60% in FDA data vs. 58% in SEER data). There was no difference in pooled outcomes among male versus female patients (PFS: hazard ratio, 0.99; 95% confidence interval, 0.92-1.07; p = .89; OS: hazard ratio, 0.99; 95% confidence interval, 0.93-1.07; p = .90). In total, 111 trials including 74,217 patients provided information on race, and 68% of patients identified as White, compared with 72.3% in US SEER incidence data. Black patients were under-represented compared with US SEER incidence data, although ethnicity was poorly reported throughout the data set. In the authors' network meta-analysis by race, there were no statistically significant differences in PFS or OS outcomes. CONCLUSIONS: No significant differences in PFS or OS outcomes were identified when the analyses were stratified by sex or race. Certain racial minorities remain under-represented, and clearer reporting of race and ethnicity is needed. Representation of female patients in FDA trials is similar to that in SEER data after adjusting for tumor type.
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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 | 0.043 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.054 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".