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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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False discovery rate-controlled multiple testing for union null hypotheses: a knockoff-based approach.

Ran Dai1, Cheng Zheng1

  • 1Department of Biostatistics, University of Nebraska Medical Center, Omaha, Nebraska, USA.

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|February 28, 2023
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Summary
This summary is machine-generated.

This study introduces Simultaneous knockoffs, a method for identifying mutual signals across multiple independent datasets. It offers exact false discovery rate (FDR) control for replicability in high-dimensional analyses.

Keywords:
FDR controlheterogeneityreplicabilityreproducibilityvariable selection

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • False discovery rate (FDR) controlling procedures are crucial for ensuring replicability in signal identification from multiple hypothesis testing.
  • High-dimensional (HD) analyses often employ FDR control to discover features associated with outcomes.
  • Independent datasets from multiple studies offer opportunities to identify signals by jointly considering heterogeneous information.

Purpose of the Study:

  • To develop a method for providing FDR control guarantees for tests of union null hypotheses of conditional independence.
  • To identify mutual signals from multiple independent datasets while ensuring statistical guarantees for replicability.

Main Methods:

  • A novel knockoff-based variable selection method, termed Simultaneous knockoffs, is presented.
  • The method provides exact FDR control guarantees under finite sample settings.
  • It is designed to work with general model settings and test statistics.

Main Results:

  • Simultaneous knockoffs successfully identifies mutual signals from multiple independent datasets.
  • The method achieves exact FDR control, enhancing the reliability of discovered signals.
  • Performance is validated through extensive numerical studies and two real-data examples.

Conclusions:

  • Simultaneous knockoffs is an effective tool for joint signal identification across multiple independent studies.
  • The method offers robust FDR control, crucial for reproducible research in high-dimensional settings.
  • This approach has broad applicability in fields utilizing multi-study data, such as cancer biomarker discovery.