A scaled kernel density estimation prior for dynamic borrowing of historical information with application to clinical trial design.

ABSTRACT: Incorporating historical data into a current data analysis can improve estimation of parameters shared across both datasets and increase the power to detect associations of interest while reducing the time and cost of new data collection. Several methods for prior distribution elicitation have been introduced to allow for the data-driven borrowing of historical information within a Bayesian analysis of the current data. We propose scaled Gaussian kernel density estimation (SGKDE) prior distributions as potentially more flexible alternatives. SGKDE priors directly use posterior samples collected from a historical data analysis to approximate probability density functions, whose variances depend on the degree of similarity between the historical and current datasets, which are used as prior

SUBMITTER: Warren JL 

PROVIDER: S-EPMC11483151 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature