 Weighted Multiple Hypothesis Testing ProceduresKang Guolian,
Ye Keying,
Liu Nianjun,
Allison David B. and
Gao Guimin
Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 24 Abstract: Multiple hypothesis testing is commonly used in genome research such as genome-wide studies and gene expression data analysis (Lin, 2005). The widely used Bonferroni procedure controls the family-wise error rate (FWER) for multiple hypothesis testing, but has limited statistical power as the number of hypotheses tested increases. The power of multiple testing procedures can be increased by using weighted p-values (Genovese et al., 2006). The weights for the p-values can be estimated by using certain prior information. Wasserman and Roeder (2006) described a weighted Bonferroni procedure, which incorporates weighted p-values into the Bonferroni procedure, and Rubin et al. (2006) and Wasserman and Roeder (2006) estimated the optimal weights that maximize the power of the weighted Bonferroni procedure under the assumption that the means of the test statistics in the multiple testing are known (these weights are called optimal Bonferroni weights). This weighted Bonferroni procedure controls FWER and can have higher power than the Bonferroni procedure, especially when the optimal Bonferroni weights are used. To further improve the power of the weighted Bonferroni procedure, first we propose a weighted idák procedure that incorporates weighted p-values into the idák procedure, and then we estimate the optimal weights that maximize the average power of the weighted idák procedure under the assumption that the means of the test statistics in the multiple testing are known (these weights are called optimal idák weights). This weighted idák procedure can have higher power than the weighted Bonferroni procedure. Second, we develop a generalized sequential (GS) idák procedure that incorporates weighted p-values into the sequential idák procedure (Scherrer, 1984). This GS idák procedure is an extension of and has higher power than the GS Bonferroni procedure of Holm (1979). Finally, under the assumption that the means of the test statistics in the multiple testing are known, we incorporate the optimal idák weights and the optimal Bonferroni weights into the GS idák procedure and the GS Bonferroni procedure, respectively. Theoretical proof and/or simulation studies show that the GS idák procedure can have higher power than the GS Bonferroni procedure when their corresponding optimal weights are used, and that both of these GS procedures can have much higher power than the weighted idák and the weighted Bonferroni procedures. All proposed procedures control the FWER well and are useful when prior information is available to estimate the weights. Keywords: weight; multiple hypothesis testing; Bonferroni procedure; Šidák procedure; family-wise error rate (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:23 Ordering information: This journal article can be ordered from Access Statistics for this article Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter |
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