Marginal Bayesian Semiparametric Modeling of Mismeasured Multivariate Interval-Censored Data.

ABSTRACT: Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards, proportional odds, and accelerated failure time (proportional quantiles) are all fit and compared. The baseline distribution is modeled as a flexible tailfree prior. The joint model is completed by considering a parametric copula function. A general misclassification model is discussed in detail, considering the possibility t

SUBMITTER: Li L 

PROVIDER: S-EPMC6711609 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature