A robust method for large-scale multiple hypotheses testing

Seungbong Han, Adin Cristian Andrei*, Kam Wah Tsui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

When drawing large-scale simultaneous inference, such as in genomics and imaging problems, multiplicity adjustments should be made, since, otherwise, one would be faced with an inflated type I error. Numerous methods are available to estimate the proportion of true null hypotheses π0, among a large number of hypotheses tested. Many methods implicitly assume that the π0 is large, that is, close to 1. However, in practice, mid-range π0 values are frequently encountered and many of the widely used methods tend to produce highly variable or biased estimates of π0. As a remedy in such situations, we propose a hierarchical Bayesian model that produces an estimator of π0 that exhibits considerably less bias and is more stable. Simulation studies seem indicative of good method performance even when low-to-moderate correlation exists among test statistics. Method performance is assessed in simulated settings and its practical usefulness is illustrated in an application to a type II diabetes study.

Original languageEnglish (US)
Pages (from-to)222-232
Number of pages11
JournalBiometrical Journal
Volume52
Issue number2
DOIs
StatePublished - Apr 2010

Keywords

  • Bayesian framework
  • Microarray data
  • Mixture model
  • Multiple testing
  • Robustness

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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