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B1327
Title: Marginal Bayesian semiparametric modelling of mismeasured multivariate interval-censored data Authors:  Alejandro Jara - Pontificia Universidad Católica de Chile (Chile) [presenting]
Abstract: 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 baseline distribution is modeled as a flexible tail free prior. The joint model is complete 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 provide empirical evidence that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters.