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

Jaeun Choi, Jianwen Cai, Donglin Zeng

Research output: Contribution to journalArticle

1 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1-27
Number of pages27
JournalSankhya B
StateAccepted/In press - Apr 28 2017


  • Generalized linear mixed model
  • Laplace approximation
  • Penalized likelihood estimator
  • Random effect
  • Simultaneous modeling
  • Stratified Cox proportional hazards model

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Penalized Likelihood Approach for Simultaneous Analysis of Survival Time and Binary Longitudinal Outcome'. Together they form a unique fingerprint.

  • Cite this