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We consider nonparametric inference for event time distributions based oncurrent status data. We show that in this scenario conventional mixture priors,including the popular Dirichlet process mixture prior, lead to biologicallyuninterpretable results as they unnaturally skew the probability mass for theevent times toward the extremes of the observed data. Simple assumptions ondependent censoring can fix the problem. We then extend the discussion tobivariate current status data with partial ordering of the two outcomes. Inaddition to dependent censoring, we also exploit some minimal known structurerelating the two event times. We design a Markov chain Monte Carlo algorithmfor posterior simulation. Applied to a recurrent infection study, the methodprovides novel insights into how symptoms-related hospital visits are affectedby covariates.

Bayesian Nonparametric Bivariate Survival Regression for Current Status Data