Markov Chains and Semi-Markov Models in Time-to-Event AnalysisErin L Abner1,4, Richard J Charnigo2,3* and Richard J Kryscio2,3,4
- *Corresponding Author:
- Richard J Charnigo
Department of Biostatistics
University of Kentucky, USA
Tel: (859) 218-2072
E-mail: [email protected]
Received date: October 21, 2013; Accepted date: October 21, 2013; Published date: October 25, 2013
Citation: Abner EL, Charnigo RJ, Kryscio RJ (2013) Markov Chains and Semi-Markov Models in Time-to-Event Analysis. J Biomet Biostat S1:e001. doi:10.4172/2155-6180.S1-e001
Copyright: © 2013 Abner EL, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.