Anthony O'Hagan, Matt Stevenson and Jason Madan
University of Sheffield, UK
Publication details: Health Economics 16, 1009-1023. 2007.
Probabilistic sensitivity analysis (PSA) is required to account for uncertainty in cost-effectiveness calculations arising from health economic models. The simplest way to perform PSA in practice is by Monte Carlo methods, which involves running the model many times using randomly sampled values of the model inputs. However, this can be impractical when the economic model takes appreciable amounts of time to run. This situation arises, in particular, for patient-level simulation models (also known as micro-simulation or individual-level simulation models), where a single run of the model simulates the health care of many thousands of individual patients. The large number of patients required in each run to achieve accurate estimation of cost-effectiveness means that only a relatively small number of runs is possible. For this reason, it is often said that PSA is not practical for patient-level models.
We develop a way to reduce the computational burden of Monte Carlo PSA for patient-level models, based on the algebra of analysis of variance and Bayesian statistics. Methods are presented to estimate the mean and variance of the model output, the cost-effectiveness acceptability curve and value of information calculations. The methods are simple to apply and will typically reduce the computational demand by a factor of at least 20. Three examples are presented.
Keywords: Analysis of variance; Bayesian statistics; cost-effectiveness; cost effectiveness acceptability curve; economic evaluation; economic model; individual-level simulation; micro-simulation; Monte Carlo; patient-level model; osteoporosis; probabilistic sensitivity analysis; rheumatoid arthritis; value of information.