Modelling and simulating early stopping of RCTs: a case study of early stop due to harm
Bibliographic record
Abstract
Despite efforts from regulatory agencies (e.g. NIH, FDA), recent systematic reviews of randomised controlled trials (RCTs) show that top medical journals continue to publish trials without requiring authors to report details for readers to evaluate early stopping decisions carefully. This article presents a systematic way of modelling and simulating interim monitoring decisions of RCTs. By taking an approach that is both general and rigorous, the proposed framework models and evaluates early stopping decisions of RCTs based on a clear and consistent set of criteria. The framework allows decision analysts to generate and quickly answer ‘what-if’ questions by simulating alternate trial scenarios. I illustrate the framework with a case study of an RCT that was stopped early due to harm. This was a trial of vitamin A supplement in relation to HIV transmission from mother-to-child through breastfeeding.
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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 | 0.093 | 0.250 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| 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; 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".