Penalized Likelihood Approach for Simultaneous Analysis of Survival Time and Binary Longitudinal Outcome

Jaeun Choi (), Jianwen Cai and Donglin Zeng
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Jaeun Choi: Albert Einstein College of Medicine
Jianwen Cai: University of North Carolina at Chapel Hill
Donglin Zeng: University of North Carolina at Chapel Hill

Sankhya B: The Indian Journal of Statistics, 2017, vol. 79, issue 2, No 2, 190-216

Abstract: Abstract In this paper we consider simultaneous analysis of survival time and binary longitudinal outcome where random effects are introduced to account for the dependence between the two different types of outcomes due to unobserved factors and assumed to follow a Gaussian distribution with mean zero. The estimator based on maximum likelihood approach using an Expectation-Maximization algorithm is consistent and asymptotically normally distributed. However, the EM algorithm may be intensive on numerical integrations with large sample sizes and large numbers of longitudinal observations per subject. We develop a more computationally efficient estimation procedure based on a penalized likelihood obtained by Laplace approximation. Through simulation studies, we compare numerical performances on the computing time, bias, and mean squared error from the proposed penalized likelihood estimation procedure and the EM algorithm of maximum likelihood estimation. We also illustrate the proposed approach with a liver transplantation data set.

Keywords: Generalized linear mixed model; Laplace approximation; Penalized likelihood estimator; Random effect; Simultaneous modeling; Stratified Cox proportional hazards model; Primary 62; Secondary H99. (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13571-017-0132-3

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