The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty. The analyses that are required to obtain the model outcomes are expressed in equations, explained in stepwise algorithms, and demonstrated in examples. Patient heterogeneity is represented by frequency distributions and analyzed with Monte Carlo simulation. Parameter uncertainty is represented by probability distributions and analyzed with 2nd-order Monte Carlo simulation (aka probabilistic sensitivity analysis). Stochastic uncertainty is analyzed with 1st-order Monte Carlo simulation (i.e., trials or random walks). This article can be used as a reference for analyzing complex models with more than one type of uncertainty and patient heterogeneity.

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Keywords Markov models, Monte Carlo method, decision making, patient heterogeneity, uncertainty, variability
Persistent URL dx.doi.org/10.1177/0272989X10381282, hdl.handle.net/1765/33765
Citation
Koerkamp, B.G, Stijnen, Th, Weinstein, M.C, & Hunink, M.G.M. (2011). The combined analysis of uncertainty and patient heterogeneity in medical decision models. Medical Decision Making: an international journal, 31(4), 650–661. doi:10.1177/0272989X10381282