Reflections on meta-analyses involving trials stopped early for benefit: Is there a problem and if so, what is it?
Bibliographic record
Abstract
We review controversies associated with randomized controlled trials (RCTs) stopped early for apparent benefit (truncated RCTs or tRCTs) and present our groups' perspective. Long-established theory, simulations and recent empirical evidence demonstrate that tRCTs will on average overestimate treatment effects, and this overestimation may be large, particularly when tRCTs have small number of events. Theoretical considerations and simulations demonstrate that on average, meta-analyses of RCTs with appropriate stopping rules will lead to only trivial overestimation of treatment effects. However, tRCTs will disproportionally contribute to meta-analytic estimates when tRCTs occur early in the sequence of trials with few subsequent studies, publication of nontruncated RCTs is delayed, there is publication bias, or tRCTs result in a 'freezing' effect in which 'correcting' trials are never undertaken. To avoid applying overestimates of effect to clinical decision-making, clinicians should view the results of individual tRCTs with small sample sizes and small number of events with skepticism. Pooled effects from meta-analyses including tRCTs are likely to overestimate effect when there is a substantial difference in effect estimates between the tRCTs and the nontruncated RCTs, and in which the tRCTs have a substantial weight in the meta-analysis despite themselves having a relatively small number of events. Such circumstances call for sensitivity analyses omitting tRCTs.
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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 | 0.687 | 0.859 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.025 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.025 | 0.050 |
| Open science | 0.018 | 0.012 |
| Research integrity | 0.040 | 0.081 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".