Monte-Carlo simulation is a form of modelling used in many areas of science where model inputs are drawn from distributions and are not treated as fixed values. Key elements of a Monte-Carlo simulation are to (a) define a domain of possible inputs (parameter); (b) generate input values randomly from probability distributions across the domain; (c) perform a deterministic computation of the model output based on the selected inputs; (d) repeat for a sufficient number of ‘draws’ of input values; (e) aggregate the results. In health care evaluations micro-simulations frequently contain Monte-Carlo elements, for example using probability distributions to construct cohorts of patients with mixes of risk factors that may impact on their future experience. Probabilistic sensitivity analysis is a form of Monte-Carlo simulation where parameter values are varied stochastically to estimate the distribution of the model output value.


How to cite: Monte-Carlo Simulation [online]. (2016). York; York Health Economics Consortium; 2016.


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