Abstract
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 language | English |
|---|---|
| Pages (from-to) | 92-100 |
| Number of pages | 9 |
| Journal | Communications in Statistics Case Studies Data Analysis and Applications |
| Volume | 2 |
| Issue number | 3-4 |
| DOIs | |
| State | Published - Oct 1 2016 |
Bibliographical note
Funding Information:This research was partially supported by grants from the National Institute on Aging (R01AG386561 and P30 AG028383) as well partial support from the National Center for Advancing Translational Sciences (UL1TR001998).
Publisher Copyright:
© 2016, © 2016 Taylor & Francis.
Keywords
- Markov chains
- Markov processes
- multi-state models
- time homogeneous
ASJC Scopus subject areas
- Statistics and Probability
- Applied Mathematics
- Analysis