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

Jara, Alejandro; Garcia Zattera, María José; Komárek, Arnost

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

Date

2015

Authors

Jara, Alejandro

Garcia Zattera, María José

Komárek, Arnost

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.

Keywords

Hazard Function, Dirichlet Process, Accelerate Failure Time Model, Gamma Process, Dirichlet Process Mixture

Citation

Alejandro Jara, María José García-Zattera, Arnost Komárek. Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data. In: Riten Mitra and Peter Mueller,editors. Nonparametric Bayesian Inference in Biostatistics. Springer; 2015. p. 247-267.