Unsuccessful trial accrual and human subjects protections: An empirical analysis of recently closed trials
Bibliographic record
Abstract
BACKGROUND: Ethical evaluation of risk-benefit in clinical trials is premised on the achievability of resolving research questions motivating an investigation. OBJECTIVE: To determine the fraction and number of patients enrolled in trials that were at risk of not meaningfully addressing their primary research objective due to unsuccessful patient accrual. METHODS: We used the National Library of Medicine clinical trial registry to capture all initiated phases 2 and 3 intervention clinical trials that were registered as closed in 2011. We then determined the number that had been terminated due to unsuccessful accrual and the number that had closed after less than 85% of the target number of human subjects had been enrolled. Five factors were tested for association with unsuccessful accrual. RESULTS: Of 2579 eligible trials, 481 (19%) either terminated for failed accrual or completed with less than 85% expected enrolment, seriously compromising their statistical power. Factors associated with unsuccessful accrual included greater number of eligibility criteria (p = 0.013), non-industry funding (25% vs 16%, p < 0.0001), earlier trial phase (23% vs 16%, p < 0.0001), fewer number of research sites at trial completion (p < 0.0001) and at registration (p < 0.0001), and an active (non-placebo) comparator (23% vs 16%, p < 0.001). CONCLUSION: A total of 48,027 patients had enrolled in trials closed in 2011 who were unable to answer the primary research question meaningfully. Ethics bodies, investigators, and data monitoring committees should carefully scrutinize trial design, recruitment plans, and feasibility of achieving accrual targets when designing and reviewing trials, monitor accrual once initiated, and take corrective action when accrual is lagging.
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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 | 0.510 | 0.848 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".