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

Jaeun Choi (), Donglin Zeng (), Andrew F. Olshan () and Jianwen Cai ()
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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

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2018, vol. 24, issue 1, No 8, 126-152

Abstract: 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).

Keywords: Gaussian mixtures; Generalized linear mixed model; Maximum likelihood estimator; Random effect; Simultaneous modeling; Stratified Cox proportional hazards model (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10985-017-9405-4

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