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Activity Number:
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166
- Non-Clinical Statistics, Personalized Medicine, and Other Topics
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Type:
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Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Biopharmaceutical Section
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Abstract #318008
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Title:
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A Meta-Inference Framework to Integrate Multiple External Models into a Current Study
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Author(s):
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Tian Gu* and Jeremy M.G. Taylor and Bhramar Mukherjee
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Companies:
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Department of Biostatistics, University of Michigan and University of Michigan and University of Michigan
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Keywords:
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Data integration;
Prediction models;
Empirical Bayes;
Meta-analysis
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Abstract:
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*Student Paper Award Winner
It has become common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized dataset collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an “internal” study while incorporating summary-level information from “external” models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it can incorporate 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 trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naïve analysis of the internal data and other naïve combinations of external estimators
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Authors who are presenting talks have a * after their name.
