Frequentist q-values for multiple-test procedures

Roger Newson

Stata Journal, 2010, vol. 10, issue 4, 568-584

Abstract: Multiple-test procedures are increasingly important as technology in- creases scientists’ ability to make large numbers of multiple measurements, as they do in genome scans. Multiple-test procedures were originally defined to input a vector of input p-values and an uncorrected critical p-value, interpreted as a fami- lywise error rate or a false discovery rate, and to output a corrected critical p-value and a discovery set, defined as the subset of input p-values that are at or below the corrected critical p-value. A range of multiple-test procedures is implemented us- ing the smileplot package in Stata (Newson and the ALSPAC Study Team 2003, Stata Journal 3: 109–132; 2010, Stata Journal 10: 691–692). The qqvalue com- mand uses an alternative formulation of multiple-test procedures, which is also used by the R function p.adjust. qqvalue inputs a variable of p-values and out- puts a variable of q-values that are equal in each observation to the minimum familywise error rate or false discovery rate that would result in the inclusion of the corresponding p-value in the discovery set if the specified multiple-test pro- cedure was applied to the full set of input p-values. Formulas and examples are presented. Copyright 2010 by StataCorp LP.

Keywords: qqvalue; smileplot; multproc; p.adjust; R; multiple-test procedure; data mining; familywise error rate; false discovery rate; Bonferroni; Sˇid ́ak; Holm; Holland; Copenhaver; Hochberg; Simes; Benjamini; Yekutieli (search for similar items in EconPapers)
Date: 2010
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