Uncertainty and Patient Heterogeneity in Medical Decision Models

Bas Groot Koerkamp, Milton C. Weinstein, Theo Stijnen, M.H. Heijenbrok-Kal and M.G. Myriam Hunink
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Bas Groot Koerkamp: Department of Surgery at Academic Medical Center, Amsterdam, The Netherlands, Program for the Assessment of Radiological Technology, Departments of Radiology and Epidemiology, Erasmus MC, Rotterdam, The Netherlands
Milton C. Weinstein: Program in Health Decision Sciences, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
Theo Stijnen: Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
M.H. Heijenbrok-Kal: Program in Health Decision Sciences, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
M.G. Myriam Hunink: Program for the Assessment of Radiological Technology, Departments of Radiology and Epidemiology, Erasmus MC, Rotterdam, The Netherlands, m.hunink@erasmusmc.nl

Medical Decision Making, 2010, vol. 30, issue 2, 194-205

Abstract: 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.

Keywords: uncertainty; patient heterogeneity; decision making; Markov models; Monte Carlo method; probabilistic sensitivity analysis; value of information analysis. (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:30:y:2010:i:2:p:194-205

DOI: 10.1177/0272989X09342277

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