PICBayes: Bayesian proportional hazards models for partly interval-censored data

Abstract

Partly interval-censored (PIC) data arise frequently in medical studies of diseases that require periodic examinations for symptoms of interest, such as progression-free survival and relapse-free survival. The proportional hazards (PH) model is the most widely used model in survival analysis. This paper introduces our new R package, PICBayes, which implements a set of functions for fitting the PH model to different complexities of partly interval-censored data under the Bayesian semiparametric framework. The main function of PICBayes is to fit (1) the PH model to PIC data; (2) the PH model with spatial frailty to areally-referenced PIC data; (3) the PH model with one random intercept to clustered PIC data; (4) the PH model with one random intercept and one random effect to clustered PIC data; and (5) general mixed effects PH model to clustered PIC data. We also included the corresponding functions for general interval-censored data. A random intercept or random effect can follow both a normal prior and a Dirichlet process mixture prior. The use of the package is illustrated by analyzing two real data sets.