Assessment of gender representation in clinical trials leading to FDA approval for oncology therapeutics between 2014 and 2019: A systematic review‐based cohort study
Bibliographic record
Abstract
BACKGROUND: Ensuring representative data accrual in clinical trials is important to safeguard the generalizability of results and to minimize disparities in care. This study's goal was to evaluate differences in gender representation in trials leading to US Food and Drug Administration (FDA) cancer drug approvals. METHODS: An observational study was conducted from January 2014 to April 2019 using PubMed and the National Institutes of Health trials registry for primary trial reports. The National Cancer Institute's Surveillance, Epidemiology, and End Results program and US Census were consulted for national cancer incidence. The outcome was an enrollment incidence disparity (EID), which was calculated as the difference between male and female trial enrollment and national incidence, with positive values representing male overrepresentation. RESULTS: There were 149 clinical trials with 59,988 participants-60.3% and 39.7% were male and female, respectively-leading to 127 oncology drug approvals. The US incidence rates were 55.4% for men versus 44.6% for women. Gender representation varied by specific tumor type. Most notably, women were underrepresented in thyroid cancer (EID, +27.4%), whereas men were underrepresented in soft tissue cancer (EID, -26.1%). Overall, women were underrepresented when compared with expected incidence (EID, +4.9%; 42% of trials). CONCLUSIONS: For many specific tumor types, women are underrepresented in clinical trials leading to FDA oncology drug approvals. It is critical to better align clinical trial cohort demographics and the populations to which these data will be extrapolated. LAY SUMMARY: This study assesses whether gender disparities exist in clinical trials leading to US Food and Drug Administration (FDA) cancer drug approvals. From January 2014 to April 2019, 149 clinical trials leading to FDA oncology drug approvals showed 60.3% and 39.7% of the enrollees were male and female, respectively. Gender representation varied by specific tumor when compared with the expected incidence rate of cancer in the United States, although women were more often underrepresented. Increased efforts are needed with regard to ensuring equitable representation in oncology clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".