Bayesian semi-parametric inference for clustered recurrent events with zero-inflation and a terminal event/4163305

TL;DR

This paper introduces a Bayesian semi-parametric model for analyzing clustered recurrent event data with zero-inflation and terminal events, accommodating hierarchical structures and heterogeneity in clinical trial settings.

Contribution

It develops a novel joint modeling approach using nonparametric Bayesian methods to handle zero-inflation, hierarchical data, and heterogeneity in recurrent and terminal event processes.

Findings

Model outperforms competing methods in simulations.

Effective in analyzing elderly fall injury prevention trial.

Provides robust inference with nonparametric Bayesian techniques.

Abstract

Recurrent event data are common in clinical studies when participants are followed longitudinally, and are often subject to a terminal event. With the increasing popularity of large pragmatic trials with a heterogeneous source population, participants are often nested in clinics and can be either susceptible or structurally unsusceptible to the recurrent process. These complications require new modeling strategies to accommodate potential zero-event inflation as well as hierarchical data structures in both the terminal and non-terminal event processes. In this paper, we develop a Bayesian semi-parametric model to jointly characterize the zero-inflated recurrent event process and the terminal event process. We use a point mass mixture of non-homogeneous Poisson processes to describe the recurrent intensity and introduce shared random effects from different sources to bridge the non-terminal and terminal event processes. To achieve robustness, we consider nonparametric Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific frailty distribution, and develop a Markov Chain Monte Carlo algorithm for posterior inference. We demonstrate the superiority of our proposed model compared with competing models via simulations and apply our method to a pragmatic cluster randomized trial for fall injury prevention among the elderly.