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

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...

Full description

Saved in:
Bibliographic Details
Published in:Communication in statistics. Case studies and data analysis 2016-10, Vol.2 (3-4), p.92-100
Main Authors: Wan, Lijie, Lou, Wenjie, Abner, Erin, Kryscio, Richard J
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2373-7484
2373-7484
DOI:10.1080/23737484.2017.1361366