SAM: Self-adapting mixture prior to dynamically borrow information from historical data in clinical trials.

ABSTRACT: Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the degree of prior-data conflict, which is often unknown beforehand, posing a significant obstacle to the application and acceptance of mixture priors. To address this challenge, we introduce self-adapting mixture (SAM) priors that determine the mixing weight using likelihood ratio test statistics or Bayes factors. SAM priors are data-driven and self-adapting, favoring the informative (noninformative) prior component when there is little (substantial) evidence of prior-data conf

SUBMITTER: Yang P 

PROVIDER: S-EPMC10842647 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature