Multiplicity-and dependency-adjusted p-values for control of the family-wise error rate

Jens Stange (), Thorsten Dickhaus, Arcadi Navarro () and Daniel Schunk ()
Additional contact information
Jens Stange: Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
Arcadi Navarro: Universitat Pompeu Fabra and Institucio Catalana de Recerca i Estudis Avancats (ICREA) and Center for Genomic Regulation (CRG), Barcelona, Spain
Daniel Schunk: Department of Economics, Johannes Gutenberg-Universitaet Mainz, Germany

No 1505, Working Papers from Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz

Abstract: We are concerned with the problem of testing multiple hypotheses simultaneously based on the same data and controlling the family-wise error rate. The multiplicity- and dependency-adjustment method (MADAM) is proposed which transforms test statistics into multiplicity- and dependency adjusted p-values. The MADAM is closely connected with the concept of the "effective number of tests", but avoids certain inconveniences of the latter. For demonstration, we apply the MADAM to data from a genetic association study by exploiting computational methods for evaluating multivariate chi-square distribution functions.

Keywords: Bonferroni correction; dependency structure; effective number of tests; genetic epidemiology; multiple testing; probability approximations; Sidak correction (search for similar items in EconPapers)
Pages: 10 pages
Date: 2015-06-29, Revised 2015-06-29
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (1)

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https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1505.pdf First version, 2015 (application/pdf)

Related works:
Journal Article: Multiplicity- and dependency-adjusted p-values for control of the family-wise error rate (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:jgu:wpaper:1505

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