scholarly journals A region-based multiple testing method for hypotheses ordered in space or time

2015 ◽  
Vol 14 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Rosa J. Meijer ◽  
Thijmen J.P. Krebs ◽  
Jelle J. Goeman
Keyword(s):  
Error Rate ◽  
Null Hypothesis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Familywise Error Rate ◽  
Expected Number ◽  
Multiple Testing Procedure ◽  
Exact Location ◽  
Testing Method

AbstractWe present a multiple testing method for hypotheses that are ordered in space or time. Given such hypotheses, the elementary hypotheses as well as regions of consecutive hypotheses are of interest. These region hypotheses not only have intrinsic meaning but testing them also has the advantage that (potentially small) signals across a region are combined in one test. Because the expected number and length of potentially interesting regions are usually not available beforehand, we propose a method that tests all possible region hypotheses as well as all individual hypotheses in a single multiple testing procedure that controls the familywise error rate. We start at testing the global null-hypothesis and when this hypothesis can be rejected we continue with further specifying the exact location/locations of the effect present. The method is implemented in the

scholarly journals TreeQTL: hierarchical error control for eQTL findings

2015 ◽  
Author(s):  
Christine Peterson ◽  
Marina Bogomolov ◽  
Yoav Benjamini ◽  
Chiara Sabatti
Keyword(s):  
Quantitative Trait Loci ◽  
Error Rate ◽  
Quantitative Trait ◽  
Multiple Testing ◽  
Error Control ◽  
R Package ◽  
Testing Procedure ◽  
Error Rates ◽  
Multiple Testing Procedure ◽  
Hierarchical Grouping

Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a stage-wise multiple testing procedure which allows control of appropriate error rates defined relative to a hierarchical grouping of the eQTL hypotheses. The R package TreeQTL is available for download at http://bioinformatics.org/treeqtl.

Multiplicity Issues in Microarray Experiments

2005 ◽  
Vol 44 (03) ◽  
pp. 431-437 ◽  
Author(s):  
J. Landgrebe ◽  
E. Brunner ◽  
F. Bretz
Keyword(s):  
Multiple Testing ◽  
Testing Procedure ◽  
Microarray Data Analysis ◽  
Familywise Error Rate ◽  
Test Procedures ◽  
Multiple Testing Procedure ◽  
P Values ◽  
Microarray Experiments ◽  
Testing Procedures ◽  
Multiple Test

Summary Objectives: Discussion of different error concepts relevant to microarray experiments. Review of some commonly used multiple testing procedures. Comparison of different approaches as applied to gene expression data. Methods: This article focuses on familywise error rate (FWER) and false discovery rate (FDR) controlling procedures. Methods under investigation include: Bonferroni-type methods and their improvements (including resampling approaches), modified Bonferroni methods, data-driven approaches, as well as the linear step-up method and its modifications. Particular emphasis lies on the description of the assumptions, advantages and limitations for the investigated methods. Results: FWER controlling procedures are often too conservative in high dimensional screening studies. A better balance between the raw P-values and the stringent FWER-adjusted P-values may be required in many situations, as provided by FDR controlling and related procedures. Conclusions: The questions remain open, which error concept to apply and which multiple testing procedure to use. Although we believe that the FDR or one of its variants will be applied more often in the future, longterm experience with microarray technology is missing and thus the validity of appropriate multiple test procedures cannot yet be assessed for microarray data analysis.

scholarly journals The Romano–Wolf multiple-hypothesis correction in Stata

2020 ◽  
Vol 20 (4) ◽  
pp. 812-843
Author(s):  
Damian Clarke ◽  
Joseph P. Romano ◽  
Michael Wolf
Keyword(s):  
Error Rate ◽  
Multiple Testing ◽  
Original Data ◽  
Dependence Structure ◽  
Familywise Error Rate ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Multiple Hypothesis ◽  
Wide Range ◽  
Performance Gains

When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

scholarly journals An Alpha-Exhaustive Multiple Testing Procedure

2016 ◽  
Vol 6 (2) ◽  
pp. 30-41
Author(s):  
Mark Chang ◽  
Xuan Deng ◽  
John Balser
Keyword(s):  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure

Fast closed testing for exchangeable local tests

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 761-768 ◽  
Author(s):  
E Dobriban
Keyword(s):  
Error Rate ◽  
Multiple Testing ◽  
Exact Algorithm ◽  
Multiple Hypothesis Testing ◽  
Test Statistics ◽  
Familywise Error Rate ◽  
Closure Principle ◽  
Worst Case ◽  
Higher Criticism ◽  
False Discovery

Summary Multiple hypothesis testing problems arise naturally in science. This note introduces a new fast closed testing method for multiple testing which controls the familywise error rate. Controlling the familywise error rate is state-of-the-art in many important application areas and is preferred over false discovery rate control for many reasons, including that it leads to stronger reproducibility. The closure principle rejects an individual hypothesis if all global nulls of subsets containing it are rejected using some test statistics. It takes exponential time in the worst case. When the tests are symmetric and monotone, the proposed method is an exact algorithm for computing the closure, is quadratic in the number of tests, and is linear in the number of discoveries. Our framework generalizes most examples of closed testing, such as Holm’s method and the Bonferroni method. As a special case of the method, we propose the Simes and higher criticism fusion test, which is powerful both for detecting a few strong signals and for detecting many moderate signals.

A factor-adjusted multiple testing procedure for ERP data analysis

2012 ◽  
Vol 44 (3) ◽  
pp. 635-643 ◽  
Author(s):  
David Causeur ◽  
Mei-Chen Chu ◽  
Shulan Hsieh ◽  
Ching-Fan Sheu
Keyword(s):  
Data Analysis ◽  
Multiple Testing ◽  
Testing Procedure ◽  
Multiple Testing Procedure

The Inheritance Procedure: Multiple Testing of Tree-structured Hypotheses

2012 ◽  
Vol 11 (1) ◽  
Author(s):  
Jelle J. Goeman ◽  
Livio Finos
Keyword(s):  
Multiple Testing ◽  
Error Control ◽  
Testing Procedure ◽  
Tree Structure ◽  
Graph Structure ◽  
Multiple Testing Procedure ◽  
Natural Way ◽  
Chromosome Level

Hypotheses tests in bioinformatics can often be set in a tree structure in a very natural way, e.g. when tests are performed at probe, gene, and chromosome level. Exploiting this graph structure in a multiple testing procedure may result in a gain in power or increased interpretability of the results.We present the inheritance procedure, a method of familywise error control for hypotheses structured in a tree. The method starts testing at the top of the tree, following up on those branches in which it finds significant results, and following up on leaf nodes in the neighborhood of those leaves. The method is a uniform improvement over a recently proposed method by Meinshausen. The inheritance procedure has been implemented in the globaltest package which is available on www.bioconductor.org.

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