On a Holm-related MTP for rejecting at least k hypotheses: general validity, optimality property, confidence regions,

Olivier J M Guilbaud1

  • 1Independent Researcher, Enköping, Sweden.

Insights

This study introduces a new multiple testing procedure (MTP) for clinical trials. This procedure offers improved power for rejecting hypotheses while maintaining strong family-wise error rate control, even with minimal assumptions.

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Statistical Inference

Background:

  • Confirmatory clinical studies often require rejecting a minimum number of hypotheses (k out of m) for success.
  • Existing Holm-type multiple testing procedures (MTPs) offer a solution but have limitations in power and flexibility.
  • There is a need for MTPs that provide sharper control and potentially higher power under minimal assumptions.

Purpose of the Study:

  • To develop and validate a novel, generally valid stepwise MTP for confirmatory studies.
  • To demonstrate strong family-wise error rate (FWER) control for the proposed MTP.
  • To enhance statistical power for rejecting at least k hypotheses compared to existing methods.

Main Methods:

  • A stepwise MTP with a gatekeeping step followed by m hypothesis testing steps.
  • Comparison of ordered p-values to optimized critical constants for rejection decisions.
  • Proof of strong FWER control without assumptions on logical or stochastic relationships between hypotheses.

Main Results:

  • The proposed MTP provides strong FWER control and is generally valid under minimal assumptions.
  • The procedure offers a sharper rejection capability, potentially rejecting fewer than k hypotheses but always rejecting at least as much as existing methods.
  • Optimality properties of the critical constants are established under a natural monotonicity restriction.

Conclusions:

  • The developed stepwise MTP offers an improvement over existing Holm-type procedures for confirmatory clinical studies.
  • This method provides enhanced statistical power and robust FWER control, applicable in various clinical trial designs.
  • The MTP is suitable for complex scenarios including superiority-noninferiority, fallback testing, and multistage gatekeeping strategies.

Related Concept Videos

Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
175
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Types of Hypothesis Testing01:11

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
26.2K
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.9K
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
4.2K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
1.9K