Bayesian Nonparametric Bivariate Survival Regression for Current Status Data∗

Giorgio Paulon, Peter Müller, Victor G. Sal y Rosas

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We consider Bayesian nonparametric inference for event time distributions based on current status data. We show that under dependent censoring 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.

Original languageEnglish
Pages (from-to)49-75
Number of pages27
JournalBayesian Analysis
Issue number1
StatePublished - 2024


  • Bayesian nonparametrics
  • current status data
  • joint modeling
  • race model
  • recurrent infections
  • survival regression


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