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

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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)

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