Joint modeling of survival and longitudinal data: Likelihood approach revisited

Fushing Hsieh, Yi Kuan Tseng, Jane Ling Wang

Research output: Contribution to journalArticlepeer-review

122 Scopus citations

Abstract

The maximum likelihood approach to jointly model the survival time and its longitudinal covariates has been successful to model both processes in longitudinal studies. Random effects in the longitudinal process are often used to model the survival times through a proportional hazards model, and this invokes an EM algorithm to search for the maximum likelihood estimates (MLEs). Several intriguing issues are examined here, including the robustness of the MLEs against departure from the normal random effects assumption, and difficulties with the profile likelihood approach to provide reliable estimates for the standard error of the MLEs. We provide insights into the robustness property and suggest to overcome the difficulty of reliable estimates for the standard errors by using bootstrap procedures. Numerical studies and data analysis illustrate our points.

Original languageEnglish
Pages (from-to)1037-1043
Number of pages7
JournalBiometrics
Volume62
Issue number4
DOIs
StatePublished - Dec 2006

Keywords

  • Joint modeling
  • Missing information principle
  • Nonparametric maximum likelihood
  • Posterior density
  • Profile likelihood

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