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Primer on Medical Decision Analysis: Part 5—Working with Markov Processes

Author

Listed:
  • David Naimark
  • Murray D. Krahn
  • Gary Naglie
  • Donald A. Redelmeier
  • Allan S. Detsky

Abstract

Clinical decisions often have long-term implications. Analysts encounter difficulties when employing conventional decision-analytic methods to model these scenarios. This occurs because probability and utility variables often change with time and conventional decision trees do not easily capture this dynamic quality. A Markov analysis performed with current computer software programs provides a flexible and convenient means of modeling long-term scenarios. However, novices should be aware of several potential pitfalls when attempting to use these programs. When deciding how to model a given clinical problem, the analyst must weigh the simplicity and clarity of a conventional tree against the fidelity of a Markov analysis. In direct comparisons, both approaches gave the same qualitative answers. Key words: decision analysis; expected value; utility; sensitivity analysis; decision trees; probability. (Med Decis Making 1997; 17:152-159)

Suggested Citation

  • David Naimark & Murray D. Krahn & Gary Naglie & Donald A. Redelmeier & Allan S. Detsky, 1997. "Primer on Medical Decision Analysis: Part 5—Working with Markov Processes," Medical Decision Making, , vol. 17(2), pages 152-159, April.
    • Handle: RePEc:sae:medema:v:17:y:1997:i:2:p:152-159
    DOI: 10.1177/0272989X9701700205
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    Cited by:

    1. Rowan Iskandar, 2018. "A theoretical foundation for state-transition cohort models in health decision analysis," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-11, December.
    2. Juhee Lee & Peter F. Thall & Bora Lim & Pavlos Msaouel, 2022. "Utility‐based Bayesian personalized treatment selection for advanced breast cancer," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1605-1622, November.

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