Bayesian Nonparametric Bivariate Survival Regression for Current Status Data

TL;DR

This paper develops a Bayesian nonparametric method for bivariate current status data, addressing issues with traditional priors and incorporating dependence and structure to improve inference in medical studies.

Contribution

It introduces a novel Bayesian approach that corrects skewed results from standard priors and extends to bivariate data with dependent censoring and known structure.

Findings

Addresses skewness in mixture priors for survival data

Extends methodology to bivariate current status data with dependence

Provides insights into infection study with covariates

Abstract

We consider nonparametric inference for event time distributions based on current status data. We show that in this scenario conventional mixture priors, including the popular Dirichlet process mixture prior, lead to biologically uninterpretable results as they unnaturally skew the probability mass for the event times toward the extremes of the observed data. Simple assumptions on dependent censoring can fix the problem. We then extend the discussion to bivariate current status data with partial ordering of the two outcomes. In addition to dependent censoring, we also exploit some minimal known structure relating the two event times. We design a Markov chain Monte Carlo algorithm for posterior simulation. Applied to a recurrent infection study, the method provides novel insights into how symptoms-related hospital visits are affected by covariates.