Bayesian Bivariate Subgroup Analysis for Risk-Benefit Evaluation

TL;DR

This paper introduces Bayesian models for joint subgroup analysis of treatment efficacy and adverse events, enabling better risk-benefit assessment in clinical trials, especially with small subgroup sizes.

Contribution

The paper presents novel Bayesian approaches for joint analysis of efficacy and harm in subgroups, improving estimation and interpretability for benefit-risk evaluation.

Findings

Effective estimation of heterogeneity in joint outcomes across subgroups.

Models accommodate small subgroup sizes with limited data.

Application to a large cardiovascular trial demonstrates practical utility.

Abstract

Subgroup analysis is a frequently used tool for evaluating heterogeneity of treatment effect and heterogeneity in treatment harm across observed baseline patient characteristics. While treatment efficacy and adverse event measures are often reported separately for each subgroup, analyzing their within-subgroup joint distribution is critical for better informed patient decision-making. In this paper, we describe Bayesian models for performing a subgroup analysis to compare the joint occurrence of a primary endpoint and an adverse event between two treatment arms. Our approaches emphasize estimation of heterogeneity in this joint distribution across subgroups, and our approaches directly accommodate subgroups with small numbers of observed primary and adverse event combinations. In addition, we describe several ways in which our models may be used to generate interpretable summary measures of benefit-risk tradeoffs for each subgroup. The methods described here are illustrated throughout using a large cardiovascular trial (N = 9,361) investigating the efficacy of an intervention for reducing systolic blood pressure to a lower-than-usual target.