Representation and analysis of medical decision problems with influence diagrams

被引:46
|
作者
Owens, DK
Shachter, RD
Nease, RF
机构
[1] VA Palo Alto Health Care System, Palo Alto, CA
[2] Section on Medical Informatics, Department of Medicine, Stanford University, Stanford, CA
[3] Dept. of Eng.-Economic Systems, Stanford University, Stanford, CA
[4] Lab. for Medical Decision Sciences, Division of General Medical Sciences, Washington University, St. Louis, MO
[5] Section of General Medicine, VA Palo Alto Health Care System, Palo Alto, CA 94304
关键词
decision analysis; influence diagrams; cost-effectiveness analysis; Bayesian updating; graphic representation;
D O I
10.1177/0272989X9701700301
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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 are 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.
引用
收藏
页码:241 / 262
页数:22
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