Primer on medical decision analysis: Part 5--Working with Markov processes.
Clinical decisions often have long-term implications. Analysis 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.
Naimark
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University of Toronto Programme in Clinical Epidemiology and Health Care Research (The Toronto Hospital and The Sunnybrook Health Science Centre Units), Ontario, Canada.
Krahn
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Naglie
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Redelmeier
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Detsky
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D001706:Biopsy; D003658:Decision Making, Computer-Assisted; D003661:Decision Support Techniques; D003663:Decision Trees; D013700:Giant Cell Arteritis / Q000150:complications / Q000188:drug therapy / Q000473:pathology; D006801:Humans; D008390:Markov Chains; D017063:Outcome Assessment, Health Care; D011241:Prednisone / Q000009:adverse effects / Q000627:therapeutic use; D011336:Probability; D012984:Software; D013699:Temporal Arteries
D011241:Prednisone