|
Sign In to gain access to subscriptions and/or personal tools.
|
Representation and Analysis of Medical Decision Problems with Influence Diagrams
Douglas K. Owens, MD, MSc
Ross D. Shachter, PhD
Robert F. Nease, JR, PhD
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)
Medical Decision Making, Vol. 17, No. 3,
241-262 (1997)
DOI: 10.1177/0272989X9701700301
 CiteULike  Complore  Connotea  Del.icio.us  Digg  Reddit  Technorati  Twitter What's this?
This article has been cited by other articles:
|
|
|
|
 
M. Gomez, C. Bielza, J. A. Fernandez del Pozo, and S. Rios-Insua
A Graphical Decision-Theoretic Model for Neonatal Jaundice
Med Decis Making,
May 1, 2007;
27(3):
250 - 265.
[Abstract]
[PDF]
|
|
|
|
|
|
|
D. C. Kendrick, D. Bu, E. Pan, and B. Middleton
Crossing the Evidence Chasm: Building Evidence Bridges from Process Changes to Clinical Outcomes
J. Am. Med. Inform. Assoc.,
May 1, 2007;
14(3):
329 - 339.
[Abstract]
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. C. Lee, E. Ekaette, K.-L. Kelly, P. Craighead, C. Newcomb, and P. Dunscombe
Implications of Cancer Staging Uncertainties in Radiation Therapy Decisions
Med Decis Making,
May 1, 2006;
26(3):
226 - 238.
[Abstract]
[PDF]
|
|
|
|
|
|
|
D. M. Bravata, K. M. McDonald, H. Szeto, W. M. Smith, C. Rydzak, and D. K. Owens
A Conceptual Framework for Evaluating Information Technologies and Decision Support Systems for Bioterrorism Preparedness and Response
Med Decis Making,
March 1, 2004;
24(2):
192 - 206.
[Abstract]
[PDF]
|
|
|
|
|
|
|
D. K. Owens
Analytic Tools for Public Health Decision Making
Med Decis Making,
October 1, 2002;
22(5_suppl):
S3 - S10.
[PDF]
|
|
|
|
|
|
|
G. D. Sanders, R. F. Nease JR, and D. K. Owens
Design and Pilot Evaluation of a System to Develop Computer-based Site-specific Practice Guidelines from Decision Models
Med Decis Making,
April 1, 2000;
20(2):
145 - 159.
[Abstract]
[PDF]
|
|
|
|
|
|
|
J.-F. Caubet
Toward Evidence-Based Statistics
Ann Intern Med,
March 21, 2000;
132(6):
507 - 508.
[Full Text]
[PDF]
|
|
|
|
|
|
|
R. F. Nease JR and D. K. Owens
Use of Influence Diagrams to Structure Medical Decisions
Med Decis Making,
July 1, 1997;
17(3):
263 - 275.
[Abstract]
[PDF]
|
|
|
|
|
|
|
M. Helfand and S. G. Pauker
Influence Diagrams:: A New Dimension for Decision Models
Med Decis Making,
July 1, 1997;
17(3):
351 - 352.
[PDF]
|
|
|
|
|
