The Futility of Futility Analyses in Adjuvant Trials in Hormone Receptor–Positive Breast Cancer
Bibliographic record
Abstract
An interim analysis is commonly used in phase III superiority trials to compare treatment arms, with the goal of terminating exposure of patients to ineffective or unsafe drugs or to identify highly effective therapies for earlier public disclosure. Traditionally, interim analyses have been designed to identify early evidence of extremely large benefit of the experimental approach, potentially leading to early dissemination of effective treatments. Increasingly, interim analysis has also involved analysis of futility, which may lead to early termination of a trial that will not yield additional useful information. This presents an important challenge in early stage hormone receptor-positive breast cancer, where recurrence often occurs late, with a steady annual event rate up to 20 years. Early analysis of events may miss late treatment effects that can be observed only with longer follow-up. We discuss approaches to futility analysis in adjuvant clinical trials in hormone receptor-positive breast cancer, the role of the Data Safety Monitoring Committee in such analyses, considerations of the potential harms vs benefits of treatment, and the risks of continuing vs early termination of a trial.
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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 | 0.650 | 0.785 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| 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".