Semiparametric Bayesian Markov analysis of personalized benefit–risk assessment

Abstract

The development of systematic and structured approaches to assess benefit–risk of medical products is a major challenge for regulatory decision makers. Existing benefit–risk methods depend only on the frequencies of mutually exclusive and exhaustive categories in which the subjects fall, and the responses of individuals are allowed to belong to any of the other categories during their postwithdrawal visits. In this article we introduce a semiparametric Bayesian Markov model (SBMM) that treats the withdrawal category as an absorbing state and analyzes subject-level data for multiple visits, accounting for any within-patient dependencies in the response profiles. A log-odds ratio model is used to model the subject-level effects by assuming a ratio of transition probabilities with respect to a “reference” category. A Dirichlet process is used as a semiparametric model for the subject-level effects to flexibly capture the underlying distributions of the personalized response profiles without making strong parametric assumptions. This also allows the borrowing of strength between the patients and achieves dimension reduction by allocating similar response profiles patterns into an unknown number of latent clusters. We analyze a motivating clinical trial dataset to assess the personalized benefit–risks in each arm and evaluate the aggregated benefits and risks associated with the drug Exalgo.