Joint model-based clustering of nonlinear longitudinal trajectories and associated time-to-event data analysis, linked by latent class membership: with application to AIDS clinical studies

Yangxin Huang (), Xiaosun Lu (), Jiaqing Chen (), Juan Liang () and Miriam Zangmeister ()
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Yangxin Huang: University of South Florida
Xiaosun Lu: Medpace Inc.
Jiaqing Chen: Wuhan University of Technology
Juan Liang: Medpace Inc.
Miriam Zangmeister: Medpace Inc.

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 4, No 12, 699-718

Abstract: Abstract Longitudinal and time-to-event data are often observed together. Finite mixture models are currently used to analyze nonlinear heterogeneous longitudinal data, which, by releasing the homogeneity restriction of nonlinear mixed-effects (NLME) models, can cluster individuals into one of the pre-specified classes with class membership probabilities. This clustering may have clinical significance, and be associated with clinically important time-to-event data. This article develops a joint modeling approach to a finite mixture of NLME models for longitudinal data and proportional hazard Cox model for time-to-event data, linked by individual latent class indicators, under a Bayesian framework. The proposed joint models and method are applied to a real AIDS clinical trial data set, followed by simulation studies to assess the performance of the proposed joint model and a naive two-step model, in which finite mixture model and Cox model are fitted separately.

Keywords: AIDS clinical trials; Bayesian analysis; Cox proportional hazards model; Longitudinal data analysis; Mixture model; Time-to-event data analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10985-017-9409-0

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