Semiparametric models of longitudinal and time-to-event data with applications to HIV viral dynamics and CD4 counts

Xiaobing Zhao*, Xian Zhou

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

We propose a semiparametric approach based on proportional hazards and copula method to jointly model longitudinal outcomes and the time-to-event. The dependence between the longitudinal outcomes on the covariates is modeled by a copula-based times series, which allows non-Gaussian random effects and overcomes the limitation of the parametric assumptions in existing linear and nonlinear random effects models. A modified partial likelihood method using estimated covariates at failure times is employed to draw statistical inference. The proposed model and method are applied to analyze a set of progression to AIDS data in a study of the association between the human immunodeficiency virus viral dynamics and the time trend in the CD4/CD8 ratio with measurement errors. Simulations are also reported to evaluate the proposed model and method.

Original languageEnglish
Pages (from-to)2461-2477
Number of pages17
JournalJournal of Applied Statistics
Volume42
Issue number11
DOIs
Publication statusPublished - 2 Nov 2015

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