A Bayesian Joint Modelling of Current Status and Current Count Data

TL;DR

This paper introduces a Bayesian joint modeling approach for current status and current count data, capturing dependence between recurring and non-recurring events, with applications demonstrated through simulations and real data analysis.

Contribution

It proposes a novel Bayesian shared frailty-based semiparametric model for jointly analyzing related event data with dependence, using an adaptive MCMC algorithm.

Findings

Effective in simulation studies

Successfully applied to fracture-osteoporosis data

Provides estimates of dependence and risk factors

Abstract

Current status censoring or case I interval censoring takes place when subjects in a study are observed just once to check if a particular event has occurred. If the event is recurring, the data are classified as current count data; if non-recurring, they are classified as current status data. Several instances of dependence of these recurring and non-recurring events are observable in epidemiology and pathology. Estimation of the degree of this dependence and identification of major risk factors for the events are the major objectives of such studies. The current study proposes a Bayesian method for the joint modelling of such related events, employing a shared frailty-based semiparametric regression model. Computational implementation makes use of an adaptive Metropolis-Hastings algorithm. Simulation studies are put into use to show the effectiveness of the method proposed and fracture-osteoporosis data are worked through to highlight its application.