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

Murray D. Krahn, MD, MSc

Gary Naglie, MD, MSc

Donald A. Redelmeier

Allan S. Detsky, MD, PhD

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)

Medical Decision Making, Vol. 17, No. 2, 152-159 (1997)
DOI: 10.1177/0272989X9701700205


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