title: Semiparametric bayes' proportional odds models for current status data with underreporting.
abstract: Current status data are a type of interval-censored event time data in which all the individuals are either left or right censored. For example, our motivation is drawn from a cross-sectional study, which measured whether or not fibroid onset had occurred by the age of an ultrasound exam for each woman. We propose a semiparametric Bayesian proportional odds model in which the baseline event time distribution is estimated nonparametrically by using adaptive monotone splines in a logistic regression model and the potential risk factors are included in the parametric part of the mean structure. The proposed approach has the advantage of being straightforward to implement using a simple and efficient Gibbs sampler, whereas alternative semiparametric Bayes' event time models encounter problems for current status data. The model is generalized to allow systematic underreporting in a subset of the data, and the methods are applied to an epidemiologic study of uterine fibroids.
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. wang99@mailbox.sc.edu
authors.forename: Lianming
authors.initials: L
authors.lastname: Wang
authors.forename: David B
authors.initials: DB
authors.lastname: Dunson
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D016021:Epidemiologic Studies; D005260:Female; D006801:Humans; D007889:Leiomyoma; D016017:Odds Ratio; D016016:Proportional Hazards Models; D012044:Regression Analysis; D012307:Risk Factors