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Semi-Markov models with phase-type sojourn distributions.

Titman, Andrew C. and Sharples, Linda D. (2010) Semi-Markov models with phase-type sojourn distributions. Biometrics, 66 (3). pp. 742-752. ISSN 1541-0420

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Abstract

Continuous-time multi-state models are widely used for categorical response data, particularly in the modeling of chronic diseases. However inference is difficult when the process is only observed at discrete time points, with no information about the times or types of events between observation times, unless a Markov assumption is made. This assumption can be limiting as rates of transition between disease states might instead depend on the time since entry into the current state. Such a formulation results in a semi-Markov model. We show that the computational problems associated with fitting semi-Markov models to panel-observed data can be alleviated by considering a class of semi-Markov models with phase-type sojourn distributions. This allows methods for hidden Markov models to be applied. In addition, extensions to models where observed states are subject to classification error are given. The methodology is demonstrated on a dataset relating to development of bronchiolitis obliterans syndrome in post-lung-transplantation patients.

Item Type: Article
Journal or Publication Title: Biometrics
Uncontrolled Keywords: Bronchiolitis obliterans syndrome ; Hidden Markov model ; Multi-state model ; Panel observation ; Phase-type distribution ; Semi-Markov model.
Subjects: H Social Sciences > HA Statistics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 27313
Deposited By: Dr Andrew Titman
Deposited On: 16 Oct 2009 16:39
Refereed?: Yes
Published?: Published
Last Modified: 09 Oct 2013 14:50
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/27313

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