Baseline demographics and disparities in cancer registration trials: An analysis of U.S. Food and Drug Administration approved drugs in 2015 and 2016.
Bibliographic record
Abstract
e18623 Background: Clinical trials play a fundamental role in the advancement of cancer care, yet specific groups remain under-represented in oncology clinical trials. We analyzed the age, race, performance status, and geographic enrollment data of cancer patients in registration trials for new drug approvals or indications by the US Food and Drug Administration (FDA) from 2015-2016. Methods: Demographic data from cancer patients enrolled in registration trials during 2015 and 2016 were analyzed. Distributions by age (65-74yo and ≥ 75yo), race (categories), performance status (ECOG ≥ 2, ECOG ≥ 3), and geographic enrollment (US/Canada, Europe, Rest of the World) were described and compared to Centers for Disease Control and Prevention (CDC) data for the general cancer population. Results: A total of 45 approved drugs investigated in 53 trials were evaluated, including 22,789 patients. Age was reported on 7850 patients, with 2524/7580 (33.3%) 65-74yo and 981/7580 (12.9%) ≥ 75yo. The race distribution for enrolled patients were 15018/18897 (79.5%) for Caucasians, 356/18897 (1.9%) for Black/AA, 2302/18897 (12.2%) for Asians and 505/18897 (2.7%) for Others. 461/19729 (2.3%) and 5/19729 (0.025%) of patients enrolled in registration trials had an ECOG ≥2 and ECOG ≥3, respectively. Portions of geographic enrollment were 3518/13192 (26.6%), 7263/13192 (55.0%), and 2379/13192 (18.0%) for US/Canada, Europe and the Rest of the World, respectively. Conclusions: These data suggest that older adults ≥75yo, blacks, and patients with co-morbid conditions are under-represented in contemporary registration clinical trials. Thus, results of recent cancer clinical trials leading to FDA drug approvals may not be generalizable to the broader cancer population in the US. Improvements in the trial enrollment process are needed to address these disparities and may be facilitated through effective health policy. Table: Comparison of age and race among clinical trial participants and all US cancer patients Registration Trials (2015-2016) CDC US Cancer Statistics (2014) 65-74yo 33.3% 28.2% ≥ 75yo 12.9% 26.5% Caucasian 79.5% 83.4% Black/AA 1.9% 10.8% Asian 12.2% 3.1% Other 2.7% 0.5%
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".