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Activity Number: 192 - Study of Health Outcomes Using Large Cohort Data
Type: Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
Sponsor: Biometrics Section
Abstract #312927
Title: Ensemble of Empirical Bayes Method to Integrate Summary-Level Information from Multiple External Studies into the Current Study
Author(s): Tian Gu* and Jeremy Taylor and Bhramar Mukherjee
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: ensemble; empirical Bayes; data integration; predictive modeling; summary-level information

Disease risk prediction models are used throughout clinical biomedicine. With the discovery of new biomarkers these models could be improved and provide better predictions. However, the data that includes the new biomarkers will typically have a limited sample size. We aim to build improved prediction models based on individual-level data from an “internal” study while incorporating summary-level information from “external” models. We propose a meta-analysis framework to perform an ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance tradeoff while preserving the most efficiency gain. The estimate we proposed is more efficient than the naïve analysis of the internal data and other naïve combinations of external estimators.

Authors who are presenting talks have a * after their name.

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