scholarly journals Strong control of the familywise error rate in observational studies that discover effect modification by exploratory methods

Biometrika ◽  
2015 ◽  
Vol 102 (4) ◽  
pp. 767-782 ◽  
Author(s):  
Jesse Y. Hsu ◽  
José R. Zubizarreta ◽  
Dylan S. Small ◽  
Paul R. Rosenbaum
Keyword(s):  
Error Rate ◽  
Observational Studies ◽  
Effect Modification ◽  
Familywise Error Rate ◽  
Strong Control

Adding dose modifications into Phase II and Phase II/III seamless trials

2019 ◽  
Vol 29 (5) ◽  
pp. 1315-1324
Author(s):  
John Spivack ◽  
Bin Cheng ◽  
Bruce Levin
Keyword(s):  
Phase Ii ◽  
Dose Response ◽  
Error Rate ◽  
Interim Analysis ◽  
Response Curve ◽  
The Other ◽  
Familywise Error Rate ◽  
Dose Selection ◽  
Dose Modifications ◽  
Strong Control

We present a technique for adding dose modifications into seamless Phase II and Phase II/III trials featuring dose selection at an interim analysis. The method is convenient to apply and can be used either in a fully prespecified, structured way or as a response to new considerations that emerge at interim. Strong control of the familywise error rate regarding false declarations of efficacy versus control is maintained. Two examples are given. One illustrates how the method could potentially “save” a trial performed in a Phase II context. The other is a seamless Phase II/III trial that uses an adaptive exploration strategy for an assumed nonmonotonic dose-response curve. It can result in greatly improved efficiency over a standard “promote the winner” rule.

scholarly journals Group sequential crossover trial designs with strong control of the familywise error rate

2018 ◽  
Vol 37 (2) ◽  
pp. 174-203 ◽  
Author(s):  
Michael J. Grayling ◽  
James M. S. Wason ◽  
Adrian P. Mander
Keyword(s):  
Error Rate ◽  
Crossover Trial ◽  
Familywise Error Rate ◽  
Group Sequential ◽  
Strong Control

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 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 Multiple Comparison with a Control Using Step-down Procedure

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Bumrungsak Phuenaree ◽  
Suttinee Kaewtaworn
Keyword(s):  
Error Rate ◽  
Single Step ◽  
Multiple Comparison ◽  
Control Group ◽  
Test Statistics ◽  
Familywise Error Rate ◽  
Dunnett Test ◽  
The Family ◽  
Bonferroni Test ◽  
Step Down

The purpose of this research was to compare the efficiency of single-step procedures and the step-down procedures in order to test for multiple comparison with a control group. Four tests; Dunnett test, Step-down Dunnett test, Bonferroni test and Bonferroni-Holm test, was considered. The performance of these tests was evaluated in terms of the family wise error rate, any-pair power and all-pairs power. A Monte Carlo simulation was performed with repeated 10,000 times. The results showed that the familywise error rate of all test statistics closed to the nominal level. The empirical power of step-down procedures were higher than the single-step procedures, and the step-down Dunnett test gave the highest power.

A primer on strong vs weak control of familywise error rate

2020 ◽  
Vol 39 (9) ◽  
pp. 1407-1413 ◽  
Author(s):  
Michael A. Proschan ◽  
Erica H. Brittain
Keyword(s):  
Error Rate ◽  
Familywise Error Rate

scholarly journals Confounding and effect measure modification in reproductive medicine research

2020 ◽  
Vol 35 (5) ◽  
pp. 1013-1018 ◽  
Author(s):  
Katharine FB Correia ◽  
Laura E Dodge ◽  
Leslie V Farland ◽  
Michele R Hacker ◽  
Elizabeth Ginsburg ◽  
...  
Keyword(s):  
Reproductive Medicine ◽  
Observational Studies ◽  
Effect Modification ◽  
Careful Consideration ◽  
Medicine Research ◽  
Effect Measure ◽  
Effect Measure Modification

Abstract The majority of research within reproductive and gynecologic health, or investigating ART, is observational in design. One of the most critical challenges for observational studies is confounding, while one of the most important for discovery and inference is effect modification. In this commentary, we explain what confounding and effect modification are and why they matter. We present examples illustrating how failing to adjust for a confounder leads to invalid conclusions, as well as examples where adjusting for a factor that is not a confounder also leads to invalid or imprecise conclusions. Careful consideration of which factors may act as confounders or modifiers of the association of interest is critical to conducting sound research, particularly with complex observational studies in reproductive medicine.

Improved power of familywise error rate procedures for discrete data under dependency

2019 ◽  
Vol 61 (1) ◽  
pp. 101-114 ◽  
Author(s):  
Li He ◽  
Joseph F. Heyse
Keyword(s):  
Error Rate ◽  
Discrete Data ◽  
Familywise Error Rate
Sign in / Sign up

Export Citation Format

Share Document