Bayesian Proportional Odds Models for Analyzing Current Status Data: Univariate, Clustered, and Multivariate

Abstract

Current status data commonly arise in many fields such as epidemiological studies and cross-sectional tumorigenicity studies. In this article, we propose a semiparametric Bayesian approach for analyzing current status data with the proportional odds model. The use of monotone splines for the baseline odds function and a novel data augmentation with Poisson latent variables enable simple updating all of the parameters in the posterior computation. The proposed approach shows good performance and is compared with the approach in Wang and Dunson (2010 Wang , L. , Dunson , D. B. ( 2010 ). Semiparametric Bayes proportional odds models for current status data with under-reporting . Biometrics. Online early, DOI: 10.1111/j.1541-0420.2010.01532.x [Web of Science ®] , [Google Scholar]) in a simulation study. We also generalize the proposed approach to analyze clustered and multivariate current status data under the frailty proportional odds models.