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Related Concept Videos

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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Cochran's Q Test01:17

Cochran's Q Test

Cochran's Q Test is a nonparametric statistical test used to determine if there are potential differences in the outcomes of three or more related groups on a binary (yes/no) or dichotomous outcome. It is essentially an extension of the McNemar Test, which is limited to two related samples - Cochran's Q test can handle three or more related samples, making it more versatile in scenarios where subjects are measured under multiple conditions. The test statistic follows a Chi-Square distribution,...

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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

Hierarchical testing of multiple endpoints in group-sequential trials.

Ekkehard Glimm1, Willi Maurer, Frank Bretz

  • 1Novartis Pharma AG, Statistical Methodology, Novartis Campus, CH-4056 Basel, Switzerland. ekkehard.glimm@novartis.com

Statistics in Medicine
|October 15, 2009
PubMed
Summary

This study explores methods for testing secondary endpoints in group-sequential clinical trials, focusing on maintaining the familywise error rate (FWER). It proposes new strategies and benchmarks for hierarchical testing to ensure trial integrity.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Group-sequential clinical trials often prioritize a primary endpoint, with interim analyses timed based on its accrual.
  • Testing secondary endpoints hierarchically requires careful consideration to maintain statistical integrity.

Purpose of the Study:

  • To investigate methods for testing secondary endpoints in group-sequential trials driven by a primary endpoint.
  • To ensure strong control of the familywise error rate (FWER) for both primary and secondary hypotheses.
  • To evaluate and propose effective multiplicity adjustment strategies.

Main Methods:

  • Systematic exploration of various multiplicity adjustment methods for hierarchical testing.
  • Derivation of an upper bound for the rejection probability in a naive testing strategy.
  • Numerical illustration using a real-world case study.

Main Results:

  • The naive strategy of testing the secondary endpoint at the alpha level upon primary endpoint significance does not maintain the FWER.
  • A sharp upper bound for the secondary endpoint's rejection probability was derived.
  • Proposed strategies offer benchmarks for maintaining FWER at alpha.

Conclusions:

  • Effective multiplicity adjustment is crucial for valid hierarchical testing of secondary endpoints in group-sequential trials.
  • The derived bounds and proposed strategies provide guidance for trial design and analysis.
  • Careful selection of testing strategies is necessary to balance statistical power and error control.