A Note on <i>P</i> ‐values for Two‐sided Tests

Finner, Helmut; Straßburger, Klaus

doi:10.1002/bimj.200710382

Cited by 8 publications

(13 citation statements)

References 5 publications

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“…This has been demonstrated in Finner et al (2010) in the context of a discrete model with one-dimensional marginal parameters. A way out of this dilemma consists in usage of so-called "realized randomized p-values" as defined and explained by Finner and Straßburger (2007) and Finner et al (2010). Although they were originally derived in terms of randomized tests, we define them here in a more general way as follows.…”

Section: Realized Randomized P-valuesmentioning

confidence: 99%

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How to analyze many contingency tables simultaneously in genetic association studies

Dickhaus

Straßburger

Schunk

et al. 2012

Statistical Applications in Genetics and Molecular Biology

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We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chisquared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are shown.

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Section: Realized Randomized P-valuesmentioning

confidence: 99%

“…: Lehmann and Romano (2005). How to calculate p-values that are compatible with such tests is, however, a topic that is still vividly discussed in the scientific community, as the discussion of Finner and Straßburger (2007) and the recent works by Rüschendorf (2009) and Habiger and Peña (2011) show.…”

Section: Realized Randomized P-valuesmentioning

confidence: 99%

How to analyze many contingency tables simultaneously in genetic association studies

Dickhaus

Straßburger

Schunk

et al. 2012

Statistical Applications in Genetics and Molecular Biology

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“…This means that, similar to non-randomized exact p-values introduced above, appropriately defined de-randomized p-values based on randomized UMPU tests are stochastically larger than U[0,1]-distributed random variables under the null hypothesis. An appropriate definition of de-randomized UMPU p-values can be found in [16] and is given as follows. Suppose X 2 = x 2 is observed.…”

Section: Tests Of Hwe and Corresponding P-valuesmentioning

confidence: 99%

How to link call rate and p‐values for Hardy–Weinberg equilibrium as measures of genome‐wide SNP data quality

Finner¹,

Straßburger²,

Heid³

et al. 2010

Statistics in Medicine

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We study the link between two quality measures of SNP (single nucleotide polymorphism) data in genome-wide association (GWA) studies, that is, per SNP call rates (CR) and p-values for testing Hardy-Weinberg equilibrium (HWE). The aim is to improve these measures by applying methods based on realized randomized p-values, the false discovery rate and estimates for the proportion of false hypotheses. While exact non-randomized conditional p-values for testing HWE cannot be recommended for estimating the proportion of false hypotheses, their realized randomized counterparts should be used. P-values corresponding to the asymptotic unconditional chi-square test lead to reasonable estimates only if SNPs with low minor allele frequency are excluded. We provide an algorithm to compute the probability that SNPs violate HWE given the observed CR, which yields an improved measure of data quality. The proposed methods are applied to SNP data from the KORA (Cooperative Health Research in the Region of Augsburg, Southern Germany) 500 K project, a GWA study in a population-based sample genotyped by Affymetrix GeneChip 500 K arrays using the calling algorithm BRLMM 1.4.0. We show that all SNPs with CR = 100 per cent are nearly in perfect HWE which militates in favor of the population to meet the conditions required for HWE at least for these SNPs. Moreover, we show that the proportion of SNPs not being in HWE increases with decreasing CR. We conclude that using a single threshold for judging HWE p-values without taking the CR into account is problematic. Instead we recommend a stratified analysis with respect to CR.

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“…Editor: Finner and Strassburger (2007) demonstrated that a randomized test (that relies on auxiliary randomization to determine rejection or non-rejection of the null hypothesis) can be more conservative than a de-randomized one. Yet this finding is not true for any meaningful comparison of a pair of corresponding tests.…”

mentioning

confidence: 99%

“…The issue is not the psychological comfort of the researcher, but rather what is best, at least in the case of clinical trials (admittedly not the application discussed by Finner and Strassburger, 2007), for the patients who rely on the results of the trials to inform their treatment decisions. If randomized tests are useful, then they should be used.…”

mentioning

confidence: 99%