Non-parametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes

Abstract

Much of survival analysis is concerned with absorbing events, i.e., subjects can only experience a single event such as mortality. This article is focused on non-absorbing or recurrent events, i.e., subjects are capable of experiencing multiple events. Recurrent events have been studied by many; however, most rely on the restrictive assumptions of linearity and proportionality. We propose a new method for analyzing recurrent events with Bayesian Additive Regression Trees (BART) avoiding such restrictive assumptions. We explore this new method via a motivating example of hospital admissions for diabetes patients and simulated data sets.

Keywords: Bayesian Additive Regression Trees; Cumulative intensity; Electronic health records (EHR); Machine learning; Non-proportional; Variable selection.