The Dangers of Stopping a Trial Too Early
Bibliographic record
Abstract
To ensure that participants in randomized controlled trials are protected from harm, interim analyses and review of results by an independent data monitoring committee have become standard practice. If an analysis of accumulating data partway through a trial reveals an unanticipated degree of benefit or toxicity, or differences in outcomes between the intervention and control groups are so unimpressive that any prospect of a positive result with the planned sample size is extremely unlikely, investigators may stop the trial earlier than originally scheduled. The practice of stopping randomized controlled trials early is, however, problematic, especially if the trial is stopped for apparent benefit. Concerns in trials stopped early for apparent benefit include appropriate interpretation of results and ethical problems concerning trial participants, clinicians, and society as a whole. In this article, we review the epidemiology of trials stopped early and illustrate some of the problems and controversies associated with stopping randomized controlled trials early for apparent benefit. Finally, we offer guidance for clinicians, those running clinical trials, and authors of systematic reviews.
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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 | 0.608 | 0.751 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".