Randomized Trials in Oncology Stopped Early for Benefit
Bibliographic record
Abstract
atedwithimprovedsurvival(hazardratio[HR],0.55;95%CI,0.38 to0.80;P.002).Giventheimplausibilityoftheseresults(because ofthelargerthanexpectedeffectonmortalityanditsinconsistency with the reduction in the risk of relapse, the key postulated mechanism for improved survival), the investigators continued the trial despite the apparent difference in survival. After random assignment of more than 1,000 patients, a significant survival advantage for five courses of consolidation chemotherapy could not be demonstrated, with an HR of 1.09 (95% CI, 0.87 to 1.37; P .4). The apparently significant results observed after few end points (ie, deaths) had occurred may be explained by data analysis at a “random high,” when a disproportionate number of events had occurred in the experimental arm by chance. Had this trial been stopped early and its results used to justify an additional course of consolidation chemotherapy, subsequent patients with acute myeloid leukemia would have been exposed to a costly, potentially toxic and ineffective therapy. The extent to which medical oncology RCTs that are stopped early for benefit manifest problems of inadequate reporting and likely effect overestimations, remains uncertain. Therefore, we examinedindetailthemedicaloncologyRCTspreviouslyreportedin the larger systematic review. 1 We reviewed the study characteristics, features related to the decision to monitor and stop the study early (sample size, interim analyses, monitoring and stopping rules), the number of events, and the estimated treatment effects reportedinthe29RCTsinmedicaloncologythathadbeenstopped early for benefit (Appendix, online only).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.160 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.072 | 0.028 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".