Bayesian Methods for Calibrating Health Policy Models: A Tutorial.

ABSTRACT: Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration

SUBMITTER: Menzies NA 

PROVIDER: S-EPMC5448142 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature