Bayesian bivariate subgroup analysis for risk–benefit evaluation

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 approach emphasizes estimation of heterogeneity in this joint distribution across subgroups, and our approach directly accommodates 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 ( $$N = 9361$$ ) investigating the efficacy of an intervention for reducing systolic blood pressure to a lower-than-usual target.