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Joint modeling of survival time and longitudinal outcomes with flexible random effects

Author

Listed:
  • Jaeun Choi

    (Albert Einstein College of Medicine)

  • Donglin Zeng

    (University of North Carolina at Chapel Hill)

  • Andrew F. Olshan

    (University of North Carolina at Chapel Hill)

  • Jianwen Cai

    (University of North Carolina at Chapel Hill)

Abstract

Joint models with shared Gaussian random effects have been conventionally used in analysis of longitudinal outcome and survival endpoint in biomedical or public health research. However, misspecifying the normality assumption of random effects can lead to serious bias in parameter estimation and future prediction. In this paper, we study joint models of general longitudinal outcomes and survival endpoint but allow the underlying distribution of shared random effect to be completely unknown. For inference, we propose to use a mixture of Gaussian distributions as an approximation to this unknown distribution and adopt an Expectation–Maximization (EM) algorithm for computation. Either AIC and BIC criteria are adopted for selecting the number of mixtures. We demonstrate the proposed method via a number of simulation studies. We illustrate our approach with the data from the Carolina Head and Neck Cancer Study (CHANCE).

Suggested Citation

  • Jaeun Choi & Donglin Zeng & Andrew F. Olshan & Jianwen Cai, 2018. "Joint modeling of survival time and longitudinal outcomes with flexible random effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 126-152, January.
    • Handle: RePEc:spr:lifeda:v:24:y:2018:i:1:d:10.1007_s10985-017-9405-4
    DOI: 10.1007/s10985-017-9405-4
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    References listed on IDEAS

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