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:
Define a domain of possible inputs (parameter). Generate input values randomly from probability distributions across the domain. Perform a deterministic computation of the model output based on the selected inputs Repeat for a sufficient number of ‘draws’ of input values Aggregate the results.
In healthcare evaluations, micro-simulations frequently contain Monte-Carlo elements, such as 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.
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