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

TL;DR

This paper introduces a Bayesian two-stage method for analyzing randomized controlled trials that improves subgroup treatment effect estimation by accounting for uncertainty in subgroup identification, demonstrated through simulations and a COVID-19 trial.

Contribution

It presents a novel Bayesian approach that integrates subgroup identification with treatment effect inference, addressing uncertainty overlooked in traditional methods.

Findings

Better calibrated confidence intervals in simulations

Substantial variation in treatment effects across subgroups in COVID-19 trial

Improved subgroup-specific inference accuracy

Abstract

An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The Likely Responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framework, and more generally, data-driven subgroup analyses, often fail to account for uncertainty in the estimation of model-based data-driven subgrouping. We propose a simple two-stage approach that integrates subgroup identification with subsequent subgroup-specific inference on treatment effects. We incorporate model estimation uncertainty from the first stage into subgroup-specific treatment effect estimation in the second stage, by utilizing Bayesian posterior distributions from the first stage. We evaluate our method through simulations, demonstrating that the proposed Bayesian two-stage model produces better calibrated confidence intervals than na\"ive approaches. We apply our method to an international COVID-19 treatment trial, which shows substantial variation in treatment effects across data-driven subgroups.