What drives cancer clinical trial accrual? An empirical analysis of studies leading to FDA authorisation (2015–2020)
Bibliographic record
Abstract
OBJECTIVE: To examine factors associated with accrual rate in industry sponsored clinical trials supporting US Food and Drug Administration (FDA) cancer drug approvals from 2015 to 2020. DESIGN, SETTING AND PARTICIPANTS: Retrospective cross-sectional study included 194 pivotal trials supporting cancer drug approvals by the US FDA from 2015 to 2020. INTERVENTIONS: Clinical trials were analysed for the type of blinding, primary endpoint, whether crossover was specified in the publication, study phase, line of therapy, response rate, investigational sites, manufacturer and randomisation ratio. MAIN OUTCOME MEASURES: The main outcome was the rate of accrual, which is the number of patients accrued in the study per open month of enrolment. RESULTS: The study consisted of 133 randomised (68%) and 61 (32%) non-randomised clinical trials. In randomised studies, we found the accrual rate was higher in trials investigating first and second line drugs (adjusted rate ratios (aRR): 1.55, 95% CI 1.18 to 2.09), phase III trials (aRR: 2.13, 95% CI 1.48 to 2.99), and for studies sponsored by Merck (aRR: 1.47, 95% CI 1.18 to 2.37), adjusting for other covariates. In contrast, the primary endpoint of a study, presence of crossover, single agent response rate, the number of investigational sites, population disease burden and skewed randomisation ratios were not associated with the rate of accrual. In the non-randomised adjusted model, the accrual rate was 2.03 higher (95% CI 1.10 to 3.92) for clinical trials sponsored by manufacturer, specifically Merck. Primary endpoint, crossover, trial phase, response rate, the number of investigational sites, disease burden or line of therapy were not associated with the rate of accrual. CONCLUSION: In this cross-sectional study, line of therapy, study phase and manufacturer were the only factors associated with accrual rate. These findings suggest many proffered factors for speedy trial accrual are not associated with greater enrolment rates.
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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 | 0.503 | 0.832 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| 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; 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".