Bayesian Nonparametric Modelling of Joint Gap Time Distributions for Recurrent Event Data

TL;DR

This paper introduces autoregressive Bayesian semi-parametric models for analyzing recurrent event data, capturing time-dependency, covariate effects, and individual clustering with efficient inference methods.

Contribution

It presents novel autoregressive Bayesian semi-parametric models that handle time-dependent gap times, covariates, and model selection for recurrent event analysis.

Findings

Models effectively capture time-dependency in recurrent events.

Method successfully incorporates covariates and handles censoring.

Applications demonstrate improved clustering and inference in medical data.

Abstract

We propose autoregressive Bayesian semi-parametric models for waiting times between recurrent events. The aim is two-fold: inference on the effect of possibly time-varying covariates on the gap times and clustering of individuals based on the time trajectory of the recurrent event. Time-dependency between gap times is taken into account through the specification of an autoregressive component for the random effects parameters influencing the response at different times. The order of the autoregression may be assumed unknown and object of inference and we consider two alternative approaches to perform model selection under this scenario. Covariates may be easily included in the regression framework and censoring and missing data are easily accounted for. As the proposed methodologies lies within the class of Dirichlet process mixtures, posterior inference can be performed through efficient MCMC algorithms. We illustrate the approach through simulations and medical applications involving recurrent hospitalizations of cancer patients and successive urinary tract infections.