Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies. Issue 15 (17th November 2016)

• Record Type: Journal Article
• Authors: Lu, Xiaosun; Huang, Yangxin; Zhou, Rong
• Is Part Of: Journal of applied statistics. Volume 43:Issue 15(2016)
• Page Start: 2713
• Page End: 2728
• Publication Date: 2016-11-17
• Subjects: AIDS clinical studies -- Bayesian inference -- finite mixture models -- joint modeling -- longitudinal data analysis -- measurement errors; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2016.1142951
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Finite mixture models are currently used to analyze heterogeneous longitudinal data. By releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, finite mixture models not only can estimate model parameters but also cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, which might be associated with a clinically important binary outcome. This article develops a joint modeling of a finite mixture of NLME models for longitudinal data in the presence of covariate measurement errors and a logistic regression for a binary outcome, linked by individual latent class indicators, under a Bayesian framework. Simulation studies are conducted to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and logistic regression are fitted separately, followed by an application to a real data set from an AIDS clinical trial, in which the viral dynamics and dichotomized time to the first decline of CD4/CD8 ratio are analyzed jointly.