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A Markov chain model used in analyzing disease history applied to a stroke study

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  • Pai-Lien Chen
  • Estrada Bernard
  • Pranab Sen

Abstract

In clinical research, study subjects may experience multiple events that are observed and recorded periodically. To analyze transition patterns of disease processes, it is desirable to use those multiple events over time in the analysis. This study proposes a multi-state Markov model with piecewise transition probability, which is able to accommodate periodically observed clinical data without a time homogeneity assumption. Models with ordinal outcomes that incorporate covariates are also discussed. The proposed models are illustrated by an analysis of the severity of morbidity in a monthly follow-up study for patients with spontaneous intracerebral hemorrhage.

Suggested Citation

  • Pai-Lien Chen & Estrada Bernard & Pranab Sen, 1999. "A Markov chain model used in analyzing disease history applied to a stroke study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(4), pages 413-422.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:4:p:413-422
    DOI: 10.1080/02664769922304
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    References listed on IDEAS

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    1. Bianca L. De Stavola, 1988. "Testing Departures from Time Homogeneity in Multistate Markov Processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 37(2), pages 242-250, June.
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