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Activity Number: 353
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #312909
Title: Systematically Integrating and Analyzing High-Dimensional Data
Author(s): Xiang Liu*+ and David Hadley and Hye-Seung Lee and Jeffrey Krischer and TEDDY Study Group The
Companies: University of South Florida and University of South Florida and University of South Florida and University of South Florida and TEDDY Study Group
Keywords: Epidemiologic studies ; Date integration ; High dimensional data ; LASSO for Cox regression
Abstract:

As epidemiologic studies begin to collect information on both genetic and environmental factors, it has become necessary to integrate the data to analyze both main and interaction effects of various factors on the development of the disease. However, the variety in the nature of data sources and the high-dimensionality of genetic data (e.g., SNPs) impose computational and statistical challenges to the integrated analysis. We discuss the complexity and scalability of three ways to integrate and analyze data. We propose a systematic approach which balances complexity and scalability when integrating data from different sources. Our proposed approach adopts advanced statistical analysis methods, including LASSO for Cox regression, to address the challenge of high-dimensionality. We use data from The Environmental Determinants of Diabetes in the Young (TEDDY) study as an example to illustrate our approach to investigate the effects of SNPs, dietary factors and their interactions on the development of islet autoimmunity. The example shows that our approach performs better in terms of practical simplicity and model fit compared to approaches based on the early and late integrations.


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