Gaussian Process Metamodeling in Bayesian Value of Information Analysis: A Case of the Complex Health Economic Model for Breast Cancer Screening

ObjectivesTo determine whether allocation of resources into further research of breast cancer screening is warranted; also, to identify the parameters, for which the information would be most valuable, to prioritize the further research if deemed justifiable.MethodsThe Bayesian value of information analysis was conducted to calculate the overall expected value of perfect information (EVPI) and the partial EVPI for the six groups of parameters. Computational expense of the partial EVPI calculation was challenged with the use of Multiple Linear Regression and Gaussian Process metamodels to significantly cut down the computing time.ResultsOf the two metamodeling techniques, the Gaussian Process was proven to perform superiorly and was therefore chosen for the partial EVPI calculation. The results indicate a considerable range in the population EVPI estimates, between €100 and €500 millions at the willingness-to-pay values between €10,000 and €40,000 per quality-adjusted life-year. The partial EVPI for the groups of parameters indicated that future research would be most valuable if directed toward obtaining more precise estimates of the cancer sojourn times. With the use of the Gaussian process metamodels, the computing time was reduced from 44 years to 47 days.ConclusionsAlthough the large values of EVPI suggest collection of further information before choosing the screening policy, it is argued that delaying the decision would result in significantly higher opportunity loss. Therefore, the best option would be to implement the most cost-effective policy given the existing information (screening women aged 40–80 years, at 3-year intervals) and simultaneously conduct observational studies alongside the implemented policy. The decision analytic model could be in this manner periodically updated with additional information as it became available and the most cost-effective policy chosen iteratively.