Efficient stratified testing procedure for a false discovery rate

Seungbong Han*, Adin Cristian Andrei, Kam Wah Tsui

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The false discovery rate (FDR) has become a popular error measure in the large-scale simultaneous testing. When data are collected from heterogenous sources and form grouped hypotheses testing, it may be beneficial to use the distinct feature of groups to conduct the multiple hypotheses testing. We propose a stratified testing procedure that uses different FDR levels according to the stratification features based on p-values. Our proposed method is easy to implement in practice. Simulations studies show that the proposed method produces more efficient testing results. The stratified testing procedure minimizes the overall false negative rate (FNR) level, while controlling the overall FDR. An example from a type II diabetes mice study further illustrates the practical advantages of this new approach.

Original languageEnglish (US)
Pages (from-to)1117-1125
Number of pages9
JournalCommunications in Statistics: Simulation and Computation
Volume44
Issue number5
DOIs
StatePublished - May 7 2015

Keywords

  • Gene expression
  • Microarray data
  • Multiple testing
  • Stratified testing

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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