|
Activity Number:
|
52
- New Challenges in Complex Data Analysis
|
|
Type:
|
Invited
|
|
Date/Time:
|
Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
|
|
Sponsor:
|
Korean International Statistical Society
|
|
Abstract #322279
|
|
|
Title:
|
Marginal Screening of 2 X 2 Tables in Large-Scale Case-Control Studies
|
|
Author(s):
|
Ian McKeague*
|
|
Companies:
|
Columbia University
|
|
Keywords:
|
Genome-wide association studies ;
Marginal screening ;
Multiple testing
|
|
Abstract:
|
Assessing statistical significance when screening large numbers of 2 x 2 tables that cross-classify disease status with different types of exposure poses a challenging multiple testing problem. The problem becomes especially acute in large-scale genetic studies. We develop a potentially more powerful and computationally efficient approach (compared with existing Bonferroni and permutation-based methods) that takes into account the presence of complex dependencies between the 2 x 2 tables. Our approach uses direct Monte Carlo simulation from the limiting null distribution of a maximally selected log-odds ratio. We apply the method to case-control data from a study of a large collection of genetic variants related to the risk of early onset stroke. The talk is based on joint work with Min Qian.
|
Authors who are presenting talks have a * after their name.
