A Bayesian likely responder approach for the analysis of randomized controlled trials

Area of Science:

• Biostatistics
• Precision Medicine
• Clinical Trial Analysis

Background:

• Precision medicine seeks to personalize treatments by identifying individuals most likely to benefit.
• The likely responder (LR) framework aids in identifying these subpopulations.
• Existing data-driven subgroup analyses often neglect uncertainty in subgroup estimation.

Purpose of the Study:

• To develop a robust statistical framework for identifying likely responders and estimating subgroup-specific treatment effects.
• To integrate subgroup identification with subgroup-specific inference while accounting for model uncertainty.
• To improve the calibration of confidence intervals in data-driven subgroup analyses.

Main Methods:

• A novel two-stage Bayesian approach combining subgroup identification and inference.
• Utilizing Bayesian posterior distributions from the first stage to incorporate model estimation uncertainty.
• Evaluating the method through simulations and application to a COVID-19 treatment trial.

Main Results:

• The proposed Bayesian two-stage model yields better-calibrated confidence intervals compared to naive approaches.
• Simulations demonstrate improved performance in handling estimation uncertainty.
• Analysis of a COVID-19 trial revealed significant variation in treatment effects across identified subgroups.

Conclusions:

• The Bayesian two-stage model offers a more reliable method for subgroup analysis in precision medicine.
• Accounting for uncertainty is crucial for accurate treatment effect estimation in data-driven subgrouping.
• This approach has potential applications in optimizing treatment strategies for various diseases, including COVID-19.