Sex Representation in Clinical Trials Associated with FDA Cancer Drug Approvals Differs Between Solid and Hematologic Malignancies
Bibliographic record
Abstract
BACKGROUND: Proportionate female representation in health research is necessary for scientific rigor and health equity. We aimed to assess the representation of women in clinical trials leading to U.S. Food and Drug Administration (FDA) cancer drug approvals. MATERIALS AND METHODS: Trials supporting FDA cancer drug approvals between July 2008 and June 2018 were sourced from PubMed and ClinicalTrials.gov. The ratio of female to male trial enrollment was compared with cancer incidence and mortality in the U.S. using International Agency for Research on Cancer data. Reproductive tract and breast cancers were excluded. Odds ratios (ORs) and 95% confidence intervals (CIs) comparing trial enrollment with population incidence and mortality were calculated. RESULTS: A total of 186 trials leading to 170 FDA cancer drug approvals showed slight female underrepresentation compared with overall cancer incidence in the U.S. (OR, 0.97; 95% CI, 0.95-0.98, p < .0001). Female enrollment for drugs approved between 2008-2013 and 2014-2018 was unchanged (OR, 1.02; 95% CI, 0.99-1.05, p = .25). There was slight female underrepresentation in hematological trials (OR, 0.95; 95% CI, 0.91-0.998; p = .040 for leukemia; OR, 0.95; 95% CI, 0.90-0.997; p = .040 for lymphoma) and significant female underrepresentation in colorectal (OR, 0.72; 95% CI, 0.69-0.76; p < .0001), pancreas (OR, 0.85; 95% CI, 0.78-0.93; p = .0004), lung (OR, 0.77; 95% CI, 0.75-0.80; p < .0001), kidney (OR, 0.63; 95% CI, 0.60-0.67; p < .0001), and thyroid cancer trials (OR, 0.26; 95% CI, 0.23-0.28; p < .0001) compared with U.S. incidence. CONCLUSION: Female underrepresentation has persisted within solid organ tumor trials but is less notable in hematologic trials. Additional work is required to identify drivers of such disparity. IMPLICATIONS FOR PRACTICE: Adequate gender representation in clinical trials is a matter of health equity. This study demonstrates that women remain underrepresented in trials across hematological and solid organ trials compared with cancer incidence and mortality in women, with the disparity worse in a number of solid organ tumor types. There are thus still significant improvements to be made regarding adequate representation of women in trials. Studies exploring the reasons for ongoing disparity in gender representation are warranted to help clinicians to rectify this.
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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 | 0.062 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".