Activity Number:
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331
- ASA Biopharmaceutical Section Student Paper Award Competition
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Type:
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Contributed
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Date/Time:
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Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #322832
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Title:
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A Synthetic Data Integration Framework to Leverage External Summary-Level Information from Heterogeneous Populations
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Author(s):
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Tian Gu* and Jeremy Taylor and Bhramar Mukherjee
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Companies:
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Harvard Chan School of Public Health and University of Michigan and University of Michigan
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Keywords:
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Data integration;
Prediction models;
Synthetic data;
Stacked multiple imputation
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Abstract:
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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.
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Authors who are presenting talks have a * after their name.