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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

Author

Listed:
  • Yangxin Huang

    (University of South Florida)

  • Xiaosun Lu

    (Medpace Inc.)

  • Jiaqing Chen

    (Wuhan University of Technology)

  • Juan Liang

    (Medpace Inc.)

  • Miriam Zangmeister

    (Medpace Inc.)

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.

Suggested Citation

  • Yangxin Huang & Xiaosun Lu & Jiaqing Chen & Juan Liang & Miriam Zangmeister, 2018. "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," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(4), pages 699-718, October.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:4:d:10.1007_s10985-017-9409-0
    DOI: 10.1007/s10985-017-9409-0
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    References listed on IDEAS

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