Principal Stratification with Time-to-Event Outcomes

TL;DR

This paper develops a Bayesian framework for principal stratification analysis of time-to-event outcomes in clinical trials, addressing intercurrent events like noncompliance and censoring.

Contribution

It introduces two causal estimands for time-to-event data within principal stratification and implements a Bayesian estimation approach using Stan and Weibull-Cox models.

Findings

Applied method to ADAPTABLE trial data.

Provided analytical and numerical solutions for causal estimands.

Demonstrated the approach's utility in real clinical trial analysis.

Abstract

Post-randomization events, also known as intercurrent events, such as treatment noncompliance and censoring due to a terminal event, are common in clinical trials. Principal stratification is a framework for causal inference in the presence of intercurrent events. Despite the extensive existing literature, there lacks generally applicable and accessible methods for principal stratification analysis with time-to-event outcomes. In this paper, we specify two causal estimands for time-to-event outcomes in principal stratification. For estimation, we adopt the general strategy of latent mixture modeling and derive the corresponding likelihood function. For computational convenience, we illustrate the general strategy with a mixture of Bayesian parametric Weibull-Cox proportional model for the outcome. We utilize the Stan programming language to obtain automatic posterior sampling of the model parameters via the Hamiltonian Monte Carlo. We provide the analytical forms of the causal estimands as functions of the model parameters and an alternative numerical method when analytical forms are not available. We apply the proposed method to the ADAPTABLE trial to evaluate the causal effect of taking 81 mg versus 325 mg aspirin on the risk of major adverse cardiovascular events.