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

Jaeun Choi, Donglin Zeng, Andrew F. Olshan, Jianwen Cai

Research output: Contribution to journalArticle

4 Scopus citations

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).

Original languageEnglish (US)
Pages (from-to)126-152
Number of pages27
JournalLifetime Data Analysis
Volume24
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • Gaussian mixtures
  • Generalized linear mixed model
  • Maximum likelihood estimator
  • Random effect
  • Simultaneous modeling
  • Stratified Cox proportional hazards model

ASJC Scopus subject areas

  • Applied Mathematics

Fingerprint Dive into the research topics of 'Joint modeling of survival time and longitudinal outcomes with flexible random effects'. Together they form a unique fingerprint.

  • Cite this