Crowdsourcing prior information to improve study design and data analysis
Though Bayesian methods are being used more frequently, many still struggle with the best method for setting priors with novel measures or task environments. We propose a method for setting priors by eliciting continuous probability distributions from naive participants. This allows us to include any relevant information participants have for a given effect. Even when prior means are near-zero, this method provides a principle way to estimate dispersion and produce shrinkage, reducing the occurrence of overestimated effect sizes. We demonstrate this method with a number of published studies and compare the effect of different prior estimation and aggregation methods.
e21543Background: Our study aims at developing a very short screening questionnaire for the preoperative evaluation of older cancer patients in the surgical clinics. Methods: Our study was based on...