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Representation and Analysis of Medical Decision Problems with Influence Diagrams

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  • Douglas K. Owens
  • Ross D. Shachter
  • Robert F. Nease JR

Abstract

Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees. Influence diagrams and decision trees are different graphic 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. The authors also contrast the information highlighted in each graphic representation, demonstrate how to calculate the expected utilities 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. They use clinical examples to illustrate the mathematical operations of the influence-diagram-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 graphic 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. Key words : decision analysis; influence diagrams; cost-effectiveness analysis; Bayesian updating; graphic representation. (Med Decis Making 1997;17:241-262)

Suggested Citation

  • Douglas K. Owens & Ross D. Shachter & Robert F. Nease JR, 1997. "Representation and Analysis of Medical Decision Problems with Influence Diagrams," Medical Decision Making, , vol. 17(3), pages 241-262, July.
    • Handle: RePEc:sae:medema:v:17:y:1997:i:3:p:241-262
    DOI: 10.1177/0272989X9701700301
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    References listed on IDEAS

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    1. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
    2. Ross D. Shachter, 1988. "Probabilistic Inference and Influence Diagrams," Operations Research, INFORMS, vol. 36(4), pages 589-604, August.
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    Cited by:

    1. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    2. Stephen G. Pauker & John B. Wong, 2005. "The Influence of Influence Diagrams in Medicine," Decision Analysis, INFORMS, vol. 2(4), pages 238-244, December.
    3. M Berkan Sesen & Ann E Nicholson & Rene Banares-Alcantara & Timor Kadir & Michael Brady, 2013. "Bayesian Networks for Clinical Decision Support in Lung Cancer Care," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    4. Douglas K. Owens, 2002. "Analytic Tools for Public Health Decision Making," Medical Decision Making, , vol. 22(1_suppl), pages 3-10, September.

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