title: A flexible AFT model for misclassified clustered interval-censored data.
abstract: 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 a sequence 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 a given 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 a simulation study to evaluate the effect of neglecting the presence of misclassification in the analysis of clustered time-to-event data.
authors.affiliation: Department of Statistics, Faculty of Mathematics, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile.
authors.forename: María José
authors.initials: MJ
authors.lastname: García-Zattera
authors.affiliation: Department of Statistics, Faculty of Mathematics, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile.
authors.forename: Alejandro
authors.initials: A
authors.lastname: Jara
authors.affiliation: Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, Sokolovska 83, CZ-186 75 Praha 8 ' Karlín, Czech Republic.
authors.forename: Arnošt
authors.initials: A
authors.lastname: Komárek
mesh_terms: D001499:Bayes Theorem; D002648:Child; D016000:Cluster Analysis; D003198:Computer Simulation; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D015233:Models, Statistical; D009909:Oral Health / Q000706:statistics & numerical data; D013997:Time Factors
keywords: Bayesian approach; Mismeasured continuous response; Multivariate survival data