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.

Additional Metadata
Keywords Decision making, Markov models, Monte Carlo method, Patient heterogeneity, Probabilistic sensitivity analysis, Uncertainty, Value of information analysis.
Persistent URL dx.doi.org/10.1177/0272989X09342277, hdl.handle.net/1765/27828
Citation
Groot Koerkamp, B, Weinstein, M.C, Stijnen, Th, Heijenbrok-Kal, M.H, & Hunink, M.G.M. (2010). Uncertainty and patient heterogeneity in medical decision models. Medical Decision Making: an international journal, 30(2), 194–205. doi:10.1177/0272989X09342277