Phase II Decision-Making Framework: Case Studies and Proof of Concept

Music City Center 

Phase II clinical trials are crucial for advancing therapeutics by identifying signals of futility, safety, and efficacy under limited data conditions. Traditional designs struggle with finite sample sizes and complex decisions. To address this, we proposed a flexible, multi-metric Bayesian framework for de-risking interim decision-making. It integrates point estimates, uncertainty, and evidence toward desired thresholds (e.g., a Target Product Profile [TPP]), ensuring transparency and interpretability. While prior evaluations used parametric multilevel model simulations, real-world applicability remained untested.

In this study, we assess the framework using real trial data from REMoxTB. By resampling data from 1,931 observed participants to emulate Phase II conditions, we evaluate Type I error, power, and sample size needs. Results show its potential to streamline decision-making, reduce sample sizes, and identify non-inferior regimens earlier. This work underscores Bayesian methodologies' value in optimizing decision-making for tuberculosis and beyond.

Bayesian statistics

Tuberculosis

Clinical trials

Decision-making

Biostatistics

Phase II trials 

Biopharmaceutical Section