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 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.
Groot Koerkamp
Bas
B
Departments of Radiology and Epidemiology, Erasmus MC, Rotterdam, The Netherlands.
Weinstein
Milton C
MC
Stijnen
Theo
T
Heijenbrok-Kal
M H
MH
Hunink
M G Myriam
MG
D000465:Algorithms; D002317:Cardiovascular Agents / Q000627:therapeutic use; D015331:Cohort Studies; D017023:Coronary Angiography; D003327:Coronary Disease / Q000188:drug therapy / Q000601:surgery; D003365:Costs and Cost Analysis; D003661:Decision Support Techniques; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D035501:Uncertainty
D002317:Cardiovascular Agents