A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations

• Activity Number: 331 - ASA Biopharmaceutical Section Student Paper Award Competition
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Tian Gu* and Jeremy Taylor and Bhramar Mukherjee
• Companies: Harvard Chan School of Public Health and University of Michigan and University of Michigan
• Keywords: Data integration; Prediction models; Synthetic data; Stacked multiple imputation

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 models. Motivated from a prostate cancer risk prediction problem where novel biomarkers are measured only in the internal study, this paper proposes an imputation-based methodology where the goal is to fit a regression model with all available predictors in the internal study while utilizing summary information from external models that may have used only a subset of the predictors. The proposed approach generates synthetic outcome data in each external population, uses stacked multiple imputation technique to create a long dataset with complete covariate information. The final analysis of the stacked imputed data is conducted by weighted regression, adjusting for heterogeneous covariate effects across populations.