Randomized Trials Stopped Early for Harm in HIV/AIDS: A Systematic Survey
Bibliographic record
Abstract
PURPOSE: The decision to stop trials early because of the harmful effects of the intervention is complex and requires weighing statistical, logistical, and ethical considerations. We assessed the prevalence of randomized clinical trials (RCTs) stopped early for harm in HIV/AIDS and determined the quality of reporting of methods to inform the decision to stop the trial. METHOD: We searched 11 electronic databases and major conference abstract databases, contacted trialist and advocacy groups, and searched the Internet. We selected RCTs stopped early for harm. We extracted data on journal and year of publication, reporting of methods and funding, planned sample size, number and planning of interim analyses, stopping rules, and effect size of the harm outcomes. RESULTS: We found 10 RCTs stopped early for harm (median, n = 85; range, 7-1227). Most interventions (n = 9) were antiviral drugs; one trial studied vitamins to prevent vertical transmission of HIV. Five studies reported a priori defined adverse events, and only 1 trial reported planned stopping guidelines. The primary harm outcomes reported across trials included toxicity, death, and increased mother-to-child transmission. Two trials were stopped due to sudden unanticipated adverse events (Stevens-Johnson syndrome, death, and encephalopathy). Relative risk point estimates for harm ranged from 1 to 6.18. Six studies reported the presence of a data safety and monitoring board. CONCLUSION: The reporting of methods to inform the decision to stop trials for harm in this population is deficient in a variety of ways, including lack of stopping guidelines. Clinicians should interpret RCTs stopped early for harm with caution and interpret the results in light of related evidence. Trialists should improve the transparency of their decision-making regarding early stopping for harmful effects.
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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 | 0.316 | 0.630 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".