Uncertainty and Patient Heterogeneity in Medical Decision Models

Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are increasingly important concepts in medical decision models. The purpose of this study is to demonstrate the various methods to analyze uncertainty and patient heterogeneity in a decision model. The authors disti...

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Bibliographic Details
Published in:Medical decision making 2010-03, Vol.30 (2), p.194-205
Main Authors: Groot Koerkamp, Bas, Weinstein, Milton C., Stijnen, Theo, Heijenbrok-Kal, M.H., Hunink, M.G. Myriam
Format: Article
Language:English
Subjects:
Algorithms
Cardiovascular Agents - therapeutic use
Cohort Studies
Coronary Angiography
Coronary Disease - drug therapy
Coronary Disease - surgery
Costs and Cost Analysis
Decision Support Techniques
Humans
Male
Markov Chains
Middle Aged
Monte Carlo Method
Uncertainty
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Summary:Parameter uncertainty, patient heterogeneity, and stochastic uncertainty of outcomes are increasingly important concepts in medical decision models. The purpose of this study is to demonstrate the various methods to analyze uncertainty and patient heterogeneity in a decision model. The authors distinguish various purposes of medical decision modeling, serving various stakeholders. Differences and analogies between the analyses are pointed out, as well as practical issues. The analyses are demonstrated with an example comparing imaging tests for patients with chest pain. For complicated analyses step-by-step algorithms are provided. The focus is on Monte Carlo simulation and value of information analysis. Increasing model complexity is a major challenge for probabilistic sensitivity analysis and value of information analysis. The authors discuss nested analyses that are required in patient-level models, and in nonlinear models for analyses of partial value of information analysis.
ISSN:0272-989X
1552-681X
DOI:10.1177/0272989X09342277