State-Space Size Considerations for Disease-Progression Models

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Authors
Regnier, Eva D.
Shechter, Steven M.
Subjects
disease progression
Markov models
state aggregation
transition probability estimation
Advisors
Date of Issue
2013-04-23
Date
April 23, 2013
Publisher
John Wiley & Sons, Ltd.
Language
Abstract
Markov models of disease progression are widely used to model transitions in patients’ health state over time. Usually, patients’ health status may be classified according to a set of ordered health states. Modelers lump together similar health states into a finite and usually small, number of health states that form the basis of a Markov chain disease-progression model. This increases the number of observations used to estimate each parameter in the transition probability matrix. However, lumping together observably distinct health states also obscures distinctions among them and may reduce the predictive power of the model. Moreover, as we demonstrate, precision in estimating the model parameters does not necessarily improve as the number of states in the model declines. This paper explores the tradeoff between lumping error introduced by grouping distinct health states and sampling error that arises when there are insufficient patient data to precisely estimate the transition probability matrix.
Type
Article
Description
The article of record as published may be found at http://dx.doi.org/10.1002/sim.5808
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
19 p.
Citation
Statistics in Medicine 32 (2013), 3862-3880
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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