title: Multigroup Equivalence Analysis for High-Dimensional Expression Data.
abstract: Hypothesis tests of equivalence are typically known for their application in bioequivalence studies and acceptance sampling. Their application to gene expression data, in particular high-dimensional gene expression data, has only recently been studied. In this paper, we examine how two multigroup equivalence tests, the F-test and the range test, perform when applied to microarray expression data. We adapted these tests to a well-known equivalence criterion, the difference ratio. Our simulation results showed that both tests can achieve moderate power while controlling the type I error at nominal level for typical expression microarray studies with the benefit of easy-to-interpret equivalence limits. For the range of parameters simulated in this paper, the F-test is more powerful than the range test. However, for comparing three groups, their powers are similar. Finally, the two multigroup tests were applied to a prostate cancer microarray dataset to identify genes whose expression follows a prespecified trajectory across five prostate cancer stages.
authors.affiliation: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Ryals School of Public Health, Birmingham, AL, USA. ; BioFire Diagnostics, LLC, Salt Lake City, UT, USA.
authors.forename: Celeste
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Ryals School of Public Health, Birmingham, AL, USA.
authors.forename: Alfred A
authors.initials: AA
authors.lastname: Bartolucci
authors.affiliation: Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Ryals School of Public Health, Birmingham, AL, USA.
authors.forename: Xiangqin
authors.initials: X
authors.lastname: Cui
keywords: F-test; equivalence; high dimension; multiple group; prostate cancer; range test