Joint analysis of nonlinear heterogeneous longitudinal data and binary outcome: an application to AIDS clinical studies |
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Authors: | Xiaosun Lu Rong Zhou |
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Institution: | Department of Biostatistics, Medpace Inc., Cincinnati, OH, USA |
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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. |
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Keywords: | AIDS clinical studies Bayesian inference finite mixture models joint modeling longitudinal data analysis measurement errors |
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