Bayesian Priors Series
A valid and reliable belief elicitation method for Bayesian priors
Bayesian inference has the advantage of formally incorporating prior beliefs about the effect of an intervention into analyses of treatment effect through the use of prior probability distributions or “priors.” Multiple methods to elicit beliefs from experts for inclusion in a Bayesian study have been used; however, the measurement properties of these methods have been infrequently evaluated. The objectives of this study were to evaluate the feasibility, validity, and reliability of a belief elicitation method for Bayesian priors.Methods to elicit beliefs for Bayesian priors: a systematic review
Bayesian analysis can incorporate clinicians' beliefs about treatment effectiveness into models that estimate treatment effects. Many elicitation methods are available, but it is unclear if any confer advantages based on principles of measurement science. We review belief-elicitation methods for Bayesian analysis and determine if any of them had an incremental value over the others based on its validity, reliability, and responsiveness.
