Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 165 - SLDS CSpeed 2
Type: Contributed
Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318887
Title: Detecting Multiple Replicating Signals Using Adaptive Filtering Procedures
Author(s): Lin Gui* and Jingshu Wang and Weijie Su and Chiara Sabatti and Art Owen
Companies: The University of Chicago and The University of Chicago and University of Pennsylvania and Stanford University and Stanford University
Keywords: simultaneous signals; high-throughput experiments; partial conjunction; composite null; multiple testing
Abstract:

Replicability is a fundamental quality of scientific discoveries: we are interested in those signals that are detectable in different laboratories, study populations, across time, etc. Unlike meta-analysis which accounts for experimental variability but does not guarantee replicability, testing a partial conjunction (PC) null aims specifically to identify the signals that are discovered in multiple studies. In many contemporary applications, e.g., comparing multiple high-throughput genetic experiments, a large number M of PC nulls need to be tested simultaneously, calling for a multiple comparisons correction. However, standard multiple testing adjustments on the M PC p-values can be severely conservative, especially when M is large and the signals are sparse. We introduce AdaFilter, a new multiple testing procedure that increases power by adaptively filtering out unlikely candidates of PC nulls. We prove that AdaFilter can control FWER and FDR as long as data across studies are independent, and has much higher power than other existing methods.


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

Back to the full JSM 2021 program