Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data

Abstract

We propose a fully nonparametric modelling approach for time-to-event regression data, when the response of interest 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 covariate-dependent time-to-event distributions are modelled using a linear dependent Dirichlet process mixture model. A general misclassification model is discussed, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. An advantage of the proposed model is that the underlying time-to-event distributions and the misclassification parameters can be estimated without any external information about the latter parameters.