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

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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).

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Acknowledgements

This research was partially supported by the National Institutes of Health grants R01 ES021900 and P01 CA142538 and the National Center for Research Resources grant UL1 RR025747.

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Authors and Affiliations

  1. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, 1300 Morris Park Avenue, New York, NY, 10461, USA

    Jaeun Choi

  2. Department of Biostatistics, University of North Carolina at Chapel Hill, McGavran-Greenberg Hl, 135 Dauer Drive, CB 7420, Chapel Hill, NC, 27599, USA

    Donglin Zeng & Jianwen Cai

  3. Department of Epidemiology, University of North Carolina at Chapel Hill, McGavran-Greenberg Hl, 135 Dauer Drive, CB 7435, Chapel Hill, NC, 27599, USA

    Andrew F. Olshan

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  1. Jaeun Choi
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  2. Donglin Zeng
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  3. Andrew F. Olshan
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  4. Jianwen Cai
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Correspondence to Jianwen Cai.

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Choi, J., Zeng, D., Olshan, A.F. et al. Joint modeling of survival time and longitudinal outcomes with flexible random effects. Lifetime Data Anal 24, 126–152 (2018). https://doi.org/10.1007/s10985-017-9405-4

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