Bayesian Optimal Phase II Design for Randomized Clinical Trials
Bibliographic record
Abstract
Randomized clinical trials are the gold standard to evaluate the efficacy of an experimental treatment. We propose a flexible Bayesian optimal phase II (BOP2) design for two-arm randomized trials. The proposed two-arm BOP2 design is flexible and can handle single, multiple primary and coprimary endpoints for superiority and noninferiority trials under a unified framework. It also allows users to specify the number and timing of interim analyses to meet clinical needs. While enjoying the flexibility of Bayesian adaptive designs, the two-arm BOP2 design explicitly controls the Type I error rate and is optimal for maximizing power, thereby ensuring desirable frequentist operating characteristics. Another feature of the two-arm BOP2 design is that its decision rule can be tabulated and included in the trial protocol prior to trial commence. To conduct the trial, no complicated Bayesian calculation is needed; clinicians can simply look up the table and make go/no-go decisions. Simulation studies show that the two-arm BOP2 design has desirable operating characteristics. Easy-to-use online application is freely available at www.trialdesign.org to facilitate the use of the two-arm BOP2 design in clinical trials.
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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 | 0.061 | 0.220 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.018 |
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".