@MISC{_dr.owens, author = {}, title = {Dr. Owens is supported by VA Health Services Research and Development Career}, year = {} }

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Abstract

complementary to decision trees. Influence diagrams and decision trees are different graphical representations for the same underlying mathematical model and operations. This article describes the elements of an influence diagram, and shows several familiar decision problems represented as decision trees and as influence diagrams. We also contrast the information highlighted in each graphical representation, demonstrate how to calculate the expected utility of decision alternatives modeled with an influence diagram, provide an overview of the conceptual basis of the solution algorithms that have been developed for influence diagrams, discuss the strengths and limitations of influence diagrams relative to decision trees, and describe the mathematical operations that are used to evaluate both decision trees and influence diagrams. We use clinical examples to illustrate the mathematical operations of the influencediagram evaluation algorithm; these operations are arc reversal, chance node removal by averaging, and decision node removal by policy determination. Influence diagrams may be helpful when problems have a high degree of conditional independence, when large models are needed, when communication of the probabilistic relationships is important, or when the analysis requires extensive Bayesian updating. The choice of graphical representation should be governed by convenience, and will depend on the problem being analyzed, on the experience of the analyst, and on the background of the consumers of the analysis. 3 1. The Influence Diagram: A Graphical Representation of Decision Models Decision models perform several functions in the analysis of medical problems. They enable clinicians and analysts to assess the expected utility of alternative actions in situations tha...