Benefit-Risk Assessment of Medical Products Using Bayesian Multi-Criteria Augmented Decision Analysis for Clinical Development
Bibliographic record
Abstract
Abstract Multi-criteria decision analysis (MCDA) is a benefit-risk assessment tool that evaluates multiple competing benefit and risk endpoints simultaneously. MCDA has the potential to aid sponsors in making effective and informed go/no-go decisions for clinical development programs. MCDA involves assigning weights to benefit and risk endpoints based on their relative importance (i.e., utility weight) and using them to compute a single utility score that represents the overall benefit-risk profile of the treatment. However, to date, MCDA applications have not been appropriate for time-to-event data. In this paper, we introduce a novel framework known as Bayesian Multi-Criteria Augmented Decision Analysis (MCADA) that extends existing probabilistic MCDA methods to encompass time-to-event and ordinal outcomes while incorporating linear and novel non-linear functions in utility aggregation. This paper provides a comprehensive description of the statistical methodology behind the MCADA framework and demonstrates its application using a simulation study, as well as two clinical trials using IPD and aggregate data. Our simulation study found that MCADA generally achieves higher power than the existing MCDA methods due to avoidance of loss of information that occurs when survival and ordinal outcomes are dichotomized. Our two case studies show that the MCADA framework can be effectively used to produce a single utility score that reflects the overall benefit-risk profile of a treatment using both IPD and aggregate data from trials. MCADA broadens the horizon of the current MCDA framework by accommodating a wider range of data types and utility functions in the utility aggregation process.
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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 | 0.032 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".