Mixture multi-state Markov regression model

Amy Ming-Fang Yen, Tony Hsiu-Hsi Chen

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Although heterogeneity across individuals may be reduced when a two-state process is extended into a multi-state process, the discrepancy between the observed and the predicted for some states may still exist owing to two possibilities, unobserved mixture distribution in the initial state and the effect of measured covariates on subsequent multi-state disease progression. In the present study, we developed a mixture Markov exponential regression model to take account of the above-mentioned heterogeneity across individuals (subject-to-subject variability) with a systematic model selection based on the likelihood ratio test. The model was successfully demonstrated by an empirical example on surveillance of patients with small hepatocellular carcinoma treated by non-surgical methods. The estimated results suggested that the model with the incorporation of unobserved mixture distribution behaves better than the one without. Complete and partial effects regarding risk factors on different subsequent multi-state transitions were identified using a homogeneous Markov model. The combination of both initial mixture distribution and homogeneous Markov exponential regression model makes a significant contribution to reducing heterogeneity across individuals and over time for disease progression.

Original languageEnglish
Pages (from-to)11-21
Number of pages11
JournalJournal of Applied Statistics
Issue number1
Publication statusPublished - Jan 2007
Externally publishedYes


  • Markov mixture model
  • Model selection
  • Multi-state

ASJC Scopus subject areas

  • Statistics and Probability


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