Marginal Bayesian Semiparametric Modeling of Mismeasured Multivariate Interval-Censored Data (304809)*Li Li, University of New Mexico
Alejandro Jara, Pontificia Universidad Catolica de Chile
Maria Garcia-Zattera, Pontificia Universidad Catolica de Chile
Timothy Hanson, Medtronic
Keywords: Mismeasured continuous response, Multivariate survival data, Population-averaged modeling, Copula function
Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-average 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 joint model is completed by considering a parametric copula function. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. We also illustrate the effect on the statistical inferences of neglecting the presence of misclassification.