A Stepwise Procedure for Toxicity Studies Based on Ratio of two means

This paper proposes a stepwise confidence set procedure for identifying equivalence or safety of compounds in a toxicity study under heteroscedasticity of variances for a normally distributed data. The problem of statistical methodology for drug safety is the control of the familywise error rate (FWER). Hence, we construct a confidence set procedure for toxicological evaluation and incorporating the partitioning principle with a case of heteroscedascity of variances under normal assumption. Our simulation studies demonstrated that the power of the procedures for heterogeneity of variances increases with increasing in ratio of means.

Download Full-text

Stepwise Procedure for Toxicological Assessment Based on Ratio of Mean Differences for a Normally Distributed Data

Asian Research Journal of Mathematics ◽

10.9734/arjom/2021/v17i1130341 ◽

2021 ◽

pp. 59-68

Author(s):

Michael J. Adjabui ◽

Jakperik Dioggban ◽

Nathaniel K. Howard

Keyword(s):

Mean Difference ◽

Distributed Data ◽

Familywise Error Rate ◽

Confidence Set ◽

Toxicological Evaluation ◽

Nominal Level ◽

Toxicological Assessment ◽

Stepwise Procedure ◽

Statistical Approaches ◽

Mean Differences

We propose a new stepwise confidence set procedure for toxicity study based on ratio of mean difference. Statistical approaches for evaluating toxicity studies that properly control familywise error rate (FWER) for difference of means between treatments and a control already exist. However, in some therapeutic areas, ratio of mean differences is desirable. Therefore, we construct stepwise confidence procedure based on Fieller's confidence intervals for multiple ratio of mean difference without multiplicity adjustment for toxicological evaluation. Simulation study revealed that the FWER is well controlled at prespecified nominal level α. Also, the power of our approach increases with increasing sample size and ratio of mean differences.

Download Full-text

The Familywise Error Rate of a Simultaneous Confidence Band for the Net Health Benefit Function

PsycEXTRA Dataset ◽

10.1037/e549302012-001 ◽

2013 ◽

Author(s):

Morris Meisner ◽

Eugene Laska ◽

Carole Siegel ◽

Joseph Wanderling

Keyword(s):

Error Rate ◽

Health Benefit ◽

Confidence Band ◽

Familywise Error Rate ◽

Benefit Function ◽

Simultaneous Confidence Band

Download Full-text

The Romano–Wolf multiple-hypothesis correction in Stata

The Stata Journal Promoting communications on statistics and Stata ◽

10.1177/1536867x20976314 ◽

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.

Download Full-text

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

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2013-0075 ◽

2015 ◽

Vol 14 (1) ◽

pp. 1-19 ◽

Cited By ~ 4

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

Download Full-text

Multiple Comparison with a Control Using Step-down Procedure

Asian Journal of Applied Sciences ◽

10.24203/ajas.v8i2.6121 ◽

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.

Download Full-text

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

Biometrika ◽

10.1093/biomet/asv034 ◽

2015 ◽

Vol 102 (4) ◽

pp. 767-782 ◽

Cited By ~ 10

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

Download Full-text

familywise error rate

The SAGE Dictionary of Statistics ◽

10.4135/9780857020123.n211 ◽

2015 ◽

Keyword(s):

Error Rate ◽

Familywise Error Rate

Download Full-text

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

Statistical Methods in Medical Research ◽

10.1177/0962280219859387 ◽

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.

Download Full-text