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A Markov Chain Model for Analysing the Progression of Patient’s Health States

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  • Jonsson, Robert

    ()
    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

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    Abstract

    Markov chains (MCs) have been used to study how the health states of patients are progressing in time. With few exceptions the studies have been based on the questionable assumptions that the MC has order m=1 and is homogeneous in time. In this paper a three-state non-homogeneous MC model is introduced that allows m to vary. It is demonstrated how wrong assumptions about homogeneity and about the value of m can invalidate predictions of future health states. This can in turn seriously bias a cost-benefit analysis when costs are attached to the predicted outcomes. The present paper only considers problems connected with model construction and estimation. Problems of testing for a proper value of m and of homogeneity is treated in a subsequent paper. Data of work resumption among sick-listed women and men are used to illustrate the theory. A nonhomogeneous MC with m = 2 was well fitted to data for both sexes. The essential difference between the rehabilitation processes for the two sexes was that men had a higher chance to move from the intermediate health state to the state ‘healthy’, while women tended to remain in the intermediate state for a longer time.

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    File URL: http://gupea.ub.gu.se/handle/2077/27932
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    Bibliographic Info

    Paper provided by Statistical Research Unit, Department of Economics, School of Business, Economics and Law, University of Gothenburg in its series Research Reports with number 2011:6.

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    Length: 32 pages
    Date of creation: 31 Oct 2011
    Date of revision:
    Handle: RePEc:hhs:gunsru:2011_006

    Contact details of provider:
    Postal: Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG
    Web page: http://www.statistics.gu.se/

    Related research

    Keywords: Rehabilitation; transition probability; prediction; Maximum Likelihood;

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