A comparison of time-homogeneous Markov chain and Markov process multi-state models

Lijie Wan, Wenjie Lou, Erin Abner, Richard J. Kryscio

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Time-homogeneous Markov models are widely used tools for analyzing longitudinal data about the progression of a chronic disease over time. There are advantages to modeling the true disease progression as a discrete time stationary Markov chain. However, one limitation of this method is its inability to handle uneven follow-up assessments or skipped visits. A continuous time version of a homogeneous Markov process multi-state model could be an alternative approach. In this article, we conduct comparisons of these two methods for unevenly spaced observations. Simulations compare the performance of the two methods and two applications illustrate the results.

Original languageEnglish
Pages (from-to)92-100
Number of pages9
JournalCommunications in Statistics Case Studies Data Analysis and Applications
Issue number3-4
StatePublished - Oct 1 2016

Bibliographical note

Publisher Copyright:
© 2016, © 2016 Taylor & Francis.


  • Markov chains
  • Markov processes
  • multi-state models
  • time homogeneous

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics
  • Analysis


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