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

David Naimark, Murray D. Krahn, Gary Naglie, Donald A. Redelmeier and Allan S. Detsky

Medical Decision Making, 1997, vol. 17, issue 2, 152-159

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)

Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:17:y:1997:i:2:p:152-159

DOI: 10.1177/0272989X9701700205

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