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Activity Number: 63 - Omics Data: Study Design, Power and Sample Size
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #330559 Presentation
Title: Power Calculation and Shrinkage in High-Throughput Screening Studies
Author(s): Noah Simon*
Companies: University of Washington
Keywords: shrinkage; empirical bayes; resampling

In calculating power, preliminary estimates of effect-size are needed. In low-dimensional problems, estimating effect-size from preliminary data can generally be done simply, and without significant bias. However, in high-throughput experiments, usual estimates of effect-size (as from eg. t-tests, regressions, etc.) result in a large (and somewhat unintuitive) bias. Calculating power using those biased effect-size-estimates results in an over-estimation of power. In this talk, we discuss a framework for using empirical Bayes and/or resampling to get a more accurate estimate of power --- these ideas are related to Stein shrinkage. We discuss this framework primarily in the case of high throughput screening (though it can apply more generally).

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

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