JSM 2011 Online Program

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Abstract Details

Activity Number: 337
Type: Topic Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #301675
Title: Novel Rank-Based Approaches for Discovery and Replication in Large-Scale Association Studies
Author(s): Dmitri Zaykin*+
Companies: National Institute of Environmental Health Sciences
Address: 111 T.W. Alexander Drive, Durham, NC, 27709,
Keywords: multiple testing ; false discovery ; follow-up studies
Abstract:

Initial findings large-scale association studies provide only tentative evidence of association. Due to uncertainty of the findings, researchers often focus on top ranking variants for replication, instead of considering significance thresholds. The rank-based approach can be used to guide both selection of top ranking variants as well as sample size allocation to discovery and replication stages. This approach utilizes the "ranking probability": chances that at least K true positives will rank among a specific number of top leads. To accommodate linkage disequilibrium (LD), ranking probabilities are usually evaluated via study-specific simulations. This approach has limited utility for design of future studies, because the data required for simulations would have yet to be collected. We derive a simple and highly accurate approximations for ranking probabilities, show that an effective number of tests can be used to accommodate LD, and evaluate consequences of ignoring LD. We illustrate how this approach can be used for planning of discovery and replication.


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