A correlated random effects model for non-homogeneous Markov processes with nonignorable missingness

Baojiang Chen, Xiao Hua Zhou

Research output: Contribution to journalArticle

3 Scopus citations


Life history data arising in clusters with pre-specified assessment time points for patients often feature incomplete data since patients may choose to visit the clinic based on their needs. Markov process models provide a useful tool describing disease progression for life history data. The literature mainly focuses on time homogeneous process. In this paper we develop methods to deal with non-homogeneous Markov process with incomplete clustered life history data. A correlated random effects model is developed to deal with the nonignorable missingness, and a time transformation is employed to address the non-homogeneity in the transition model. Maximum likelihood estimate based on the Monte-Carlo EM algorithm is advocated for parameter estimation. Simulation studies demonstrate that the proposed method works well in many situations. We also apply this method to an Alzheimer's disease study.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalJournal of Multivariate Analysis
Publication statusPublished - May 1 2013



  • Cluster
  • Markov non-homogeneous
  • Missing not at random
  • Random effects
  • Transition intensity

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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