Abstrakt
Motivated by a longitudinal oral health study, we propose a flexible modeling approach for clustered time-to-event data, when the response of interest can only be determined to lie in an interval obtained from asequence of examination times (interval-censored data) and on top of that, the determination of the occurrence of the event is subject to misclassification. The clustered time-to-event data are modeled using an accelerated failure time model with random effects and by assuming a penalized Gaussian mixture model for the random effects terms to avoid restrictive distributional assumptions concerning the event times.
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 agiven subject across time. A Bayesian implementation of the proposed model is described in a detailed manner.
We additionally provide empirical evidence showing 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. We also provide results of asimulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data.