A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.

ABSTRACT: There is a growing need for flexible general frameworks that integrate individual-level data with external summary information for improved statistical inference. External information relevant for a risk prediction model may come in multiple forms, through regression coefficient estimates or predicted values of the outcome variable. Different external models may use different sets of predictors and the algorithm they used to predict the outcome Y given these predictors may or may not be known. The underlying populations corresponding to each external model may be different from each other and from the internal study population. Motivated by a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based metho

SUBMITTER: Gu T 

PROVIDER: S-EPMC10480346 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature