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Semi-Markov models with phase-type sojourn distributions.

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Semi-Markov models with phase-type sojourn distributions. / Titman, Andrew C.; Sharples, Linda D.
In: Biometrics, Vol. 66, No. 3, 09.2010, p. 742-752.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Titman, AC & Sharples, LD 2010, 'Semi-Markov models with phase-type sojourn distributions.', Biometrics, vol. 66, no. 3, pp. 742-752. https://doi.org/10.1111/j.1541-0420.2009.01339.x

APA

Titman, A. C., & Sharples, L. D. (2010). Semi-Markov models with phase-type sojourn distributions. Biometrics, 66(3), 742-752. https://doi.org/10.1111/j.1541-0420.2009.01339.x

Vancouver

Titman AC, Sharples LD. Semi-Markov models with phase-type sojourn distributions. Biometrics. 2010 Sept;66(3):742-752. doi: 10.1111/j.1541-0420.2009.01339.x

Author

Titman, Andrew C. ; Sharples, Linda D. / Semi-Markov models with phase-type sojourn distributions. In: Biometrics. 2010 ; Vol. 66, No. 3. pp. 742-752.

Bibtex

@article{1c1e85d1a1be49ccbc2cabf74e1a44e8,
title = "Semi-Markov models with phase-type sojourn distributions.",
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.",
keywords = "Bronchiolitis obliterans syndrome, Hidden Markov model, Multi-state model, Panel observation, Phase-type distribution, Semi-Markov model.",
author = "Titman, {Andrew C.} and Sharples, {Linda D.}",
year = "2010",
month = sep,
doi = "10.1111/j.1541-0420.2009.01339.x",
language = "English",
volume = "66",
pages = "742--752",
journal = "Biometrics",
issn = "1541-0420",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Semi-Markov models with phase-type sojourn distributions.

AU - Titman, Andrew C.

AU - Sharples, Linda D.

PY - 2010/9

Y1 - 2010/9

N2 - 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.

AB - 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.

KW - Bronchiolitis obliterans syndrome

KW - Hidden Markov model

KW - Multi-state model

KW - Panel observation

KW - Phase-type distribution

KW - Semi-Markov model.

U2 - 10.1111/j.1541-0420.2009.01339.x

DO - 10.1111/j.1541-0420.2009.01339.x

M3 - Journal article

VL - 66

SP - 742

EP - 752

JO - Biometrics

JF - Biometrics

SN - 1541-0420

IS - 3

ER -