Bayesian semiparametric inference for multivariate doubly-interval-censored data

TL;DR

This paper introduces a Bayesian semiparametric model for analyzing multivariate doubly-interval-censored data, allowing flexible estimation of survival curves without traditional hazard assumptions, demonstrated on dental study data.

Contribution

It develops a novel dependent Bayesian semiparametric approach for multivariate censored data, avoiding common hazard assumptions and capturing dependencies among multiple events.

Findings

Flexible survival curve estimation without proportional hazards assumptions

Effective modeling of dependent multivariate censored data

Application to dental caries data demonstrating method's utility

Abstract

Based on a data set obtained in a dental longitudinal study, conducted in Flanders (Belgium), the joint time to caries distribution of permanent first molars was modeled as a function of covariates. This involves an analysis of multivariate continuous doubly-interval-censored data since: (i) the emergence time of a tooth and the time it experiences caries were recorded yearly, and (ii) events on teeth of the same child are dependent. To model the joint distribution of the emergence times and the times to caries, we propose a dependent Bayesian semiparametric model. A major feature of the proposed approach is that survival curves can be estimated without imposing assumptions such as proportional hazards, additive hazards, proportional odds or accelerated failure time.