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Activity Number: 152 - Frontiers of High-Dimensional and Complex Data analysis
Type: Topic Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: International Chinese Statistical Association
Abstract #327276
Title: Data Enriched Generalized Linear Methods
Author(s): Sayan Dasgupta* and Cheng Zheng and Ying Qing Chen and Asad Haris
Companies: Fred Hutchinson Cancer Research Center and University of Wisconsin at Milwaukee and Fred Hutchinson Cancer Research Center and University of Washington
Keywords: big data; data enriched regression; clinical trials; observational data; shrinkage

In infectious disease research, candidate preventive interventions are usually assessed by randomized clinical trials on disease outcomes. However, these trials tend to be small, and may be compromised by insufficient power due to a number of factors, which in turn will reduce their actual population impact. One way to possibly circumvent the power loss is by harnessing external data that contain valuable information on the related trials. Although not of the same scientific rigor as that of randomized clinical trials, they still may contain valuable information about disease outcomes caused by the pathogen of interest, patient characteristics, and/or candidate preventive interventions. Taking advantage of this information in the external big data, we develop both regularization and data-driven weighting methods to improve estimation of efficacy and risk prediction in the randomized clinical trials. Among regularization methods, we use L1 and L2 penalized shrinkage methods as well as their combination. We also propose a novel weighted shrinkage estimator, based on first-order approximation and its higher order versions, and compare it with the James-Stein type shrinkage estimator.

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

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