scholarly journals Multiple testing procedures for adaptive enrichment designs: combining group sequential and reallocation approaches

Biostatistics ◽  
2016 ◽  
Vol 17 (4) ◽  
pp. 650-662 ◽  
Author(s):  
Michael Rosenblum ◽  
Tianchen Qian ◽  
Yu Du ◽  
Huitong Qiu ◽  
Aaron Fisher
Keyword(s):  
Multiple Testing ◽  
Group Sequential ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Enrichment Designs ◽  
Adaptive Enrichment Designs

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.

Joint testing of overall and simple effects for the two-by-two factorial trial design

2021 ◽  
Vol 18 (5) ◽  
pp. 521-528
Author(s):  
Eric S Leifer ◽  
James F Troendle ◽  
Alexis Kolecki ◽  
Dean A Follmann
Keyword(s):  
Blood Pressure ◽  
Blood Pressure Control ◽  
Multiple Testing ◽  
Pressure Control ◽  
Factorial Analysis ◽  
Testing Procedures ◽  
Type 1 Error ◽  
Multiple Testing Procedures ◽  
To Receive

Background/aims: The two-by-two factorial design randomizes participants to receive treatment A alone, treatment B alone, both treatments A and B( AB), or neither treatment ( C). When the combined effect of A and B is less than the sum of the A and B effects, called a subadditive interaction, there can be low power to detect the A effect using an overall test, that is, factorial analysis, which compares the A and AB groups to the C and B groups. Such an interaction may have occurred in the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD BP) which simultaneously randomized participants to receive intensive or standard blood pressure, control and intensive or standard glycemic control. For the primary outcome of major cardiovascular event, the overall test for efficacy of intensive blood pressure control was nonsignificant. In such an instance, simple effect tests of A versus C and B versus C may be useful since they are not affected by a subadditive interaction, but they can have lower power since they use half the participants of the overall trial. We investigate multiple testing procedures which exploit the overall tests’ sample size advantage and the simple tests’ robustness to a potential interaction. Methods: In the time-to-event setting, we use the stratified and ordinary logrank statistics’ asymptotic means to calculate the power of the overall and simple tests under various scenarios. We consider the A and B research questions to be unrelated and allocate 0.05 significance level to each. For each question, we investigate three multiple testing procedures which allocate the type 1 error in different proportions for the overall and simple effects as well as the AB effect. The Equal Allocation 3 procedure allocates equal type 1 error to each of the three effects, the Proportional Allocation 2 procedure allocates 2/3 of the type 1 error to the overall A (respectively, B) effect and the remaining type 1 error to the AB effect, and the Equal Allocation 2 procedure allocates equal amounts to the simple A (respectively, B) and AB effects. These procedures are applied to ACCORD BP. Results: Across various scenarios, Equal Allocation 3 had robust power for detecting a true effect. For ACCORD BP, all three procedures would have detected a benefit of intensive glycemia control. Conclusions: When there is no interaction, Equal Allocation 3 has less power than a factorial analysis. However, Equal Allocation 3 often has greater power when there is an interaction. The R package factorial2x2 can be used to explore the power gain or loss for different scenarios.

scholarly journals Chicdiff: a computational pipeline for detecting differential chromosomal interactions in Capture Hi-C data

2019 ◽  
Vol 35 (22) ◽  
pp. 4764-4766 ◽  
Author(s):  
Jonathan Cairns ◽  
William R Orchard ◽  
Valeriya Malysheva ◽  
Mikhail Spivakov
Keyword(s):  
Multiple Testing ◽  
Positive Association ◽  
R Package ◽  
Supplementary Information ◽  
Gene Promoters ◽  
Robust Detection ◽  
Testing Procedures ◽  
Multiple Testing Procedures ◽  
Powerful Approach ◽  
Cell Type Specific

Abstract Summary Capture Hi-C is a powerful approach for detecting chromosomal interactions involving, at least on one end, DNA regions of interest, such as gene promoters. We present Chicdiff, an R package for robust detection of differential interactions in Capture Hi-C data. Chicdiff enhances a state-of-the-art differential testing approach for count data with bespoke normalization and multiple testing procedures that account for specific statistical properties of Capture Hi-C. We validate Chicdiff on published Promoter Capture Hi-C data in human Monocytes and CD4+ T cells, identifying multitudes of cell type-specific interactions, and confirming the overall positive association between promoter interactions and gene expression. Availability and implementation Chicdiff is implemented as an R package that is publicly available at https://github.com/RegulatoryGenomicsGroup/chicdiff. Supplementary information Supplementary data are available at Bioinformatics online.

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