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Mathematical Models in Decision Analysis

Published online by Cambridge University Press:  02 January 2015

Eric Tom and
Kevin A. Schulman
Kevin A. Schulman*
Affiliation:
Clinical Economics Research Unit, Division of General Internal Medicine, Georgetown University Medical Center, Washington, DC
*
Clinical Economics Research Unit, Georgetown University Medical Center, 2233 Wisconsin Ave NW, Suite 440, Washington, DC 20007
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Abstract

Decision analysis offers powerful techniques to understand and evaluate uncertain clinical situations better. Decision analytic models are appearing with increasing frequency in health policy planning, clinical information and decision-support computer systems, evaluations of clinical pathways, development of clinical practice or utilization review guidelines, and epidemiologic research. This article describes the structure, application, and limitations of the more popular decision analytic methods, including decision trees, Markov models, Monte Carlo simulation, survival and hazard functions, fuzzy logic, and sensitivity analysis. Understanding the nature of these methods will help readers to assess better the appropriateness of their use in published reports.

Information

Type
Statistics for Hospital Epidemiology
Information
Infection Control & Hospital Epidemiology , Volume 18 , Issue 1 , January 1997, pp. 65 - 73
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1997 

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