A Bayesian adaptive design for clinical trials of rare efficacy outcomes with multiple definitions
Bibliographic record
Abstract
INTRODUCTION: Bayesian adaptive designs for clinical trials have gained popularity in the recent years due to the flexibility and efficiency that they offer. We consider the scenario where the outcome of interest comprises events with relatively low risk of occurrence and different case definitions resulting in varying control group risk assumptions. This is a scenario that occurs frequently for infectious diseases in global health research. METHODS: We propose a Bayesian adaptive design that incorporates different case definitions of the outcome of interest that vary in stringency. A set of stopping rules are proposed where superiority and futility may be concluded with respect to different outcome definitions and therefore maintain a realistic probability of stopping in trials with low event rates. Through a simulation study, a variety of stopping rules and design configurations are compared. RESULTS: The simulation results are provided in an interactive web application that allows the user to explore and compare the design operating characteristics for a variety of assumptions and design parameters with respect to different outcome definitions. The results for select simulation scenarios are provided in the article. DISCUSSION: Bayesian adaptive designs offer the potential for maximizing the information learned from the data collected through clinical trials. The proposed design enables monitoring and utilizing multiple composite outcomes based on rare events to optimize the trial design operating characteristics.
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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 | 0.047 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".