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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...
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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Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
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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...
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Survival Tree

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Survival Curves

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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

A graphical approach to sequentially rejective multiple test procedures.

Frank Bretz1, Willi Maurer, Werner Brannath

  • 1Novartis Pharma AG, Lichtstrasse 35, 4002 Basel, Switzerland.

Statistics in Medicine
|December 4, 2008
PubMed
Summary
This summary is machine-generated.

We introduce a graphical method for constructing and performing multiple testing procedures in clinical trials. This approach simplifies complex Bonferroni-type tests, improving communication and application for various research questions.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Inference

Background:

  • Multiple testing procedures are crucial for clinical trials with multiple endpoints or treatment arms.
  • Existing methods like gatekeeping, fixed sequence, and fallback procedures can be complex to specify and communicate due to reliance on the closed test principle.
  • There is a need for more intuitive and adaptable multiple test procedures in clinical research.

Purpose of the Study:

  • To propose a simple iterative graphical approach for constructing and performing Bonferroni-type multiple tests.
  • To represent these multiple test procedures using directed, weighted graphs for enhanced clarity.
  • To demonstrate the application of this graphical method in tailoring procedures to specific study objectives.

Main Methods:

  • Developed a graphical approach representing multiple test procedures as directed, weighted graphs.
  • Each node in the graph corresponds to an elementary hypothesis.
  • Proposed a sequential algorithm for generating these graphs and testing hypotheses.

Main Results:

  • The graphical approach simplifies the construction and communication of complex multiple testing procedures.
  • Visualizations of common gatekeeping strategies were generated using this method.
  • A case study demonstrated the adaptability of the approach for specific clinical trial objectives.

Conclusions:

  • The proposed graphical method offers a more intuitive and communicable alternative to traditional Bonferroni-type tests.
  • This approach facilitates the tailoring of multiple test procedures to the unique objectives of clinical trials.
  • The use of directed, weighted graphs enhances the understanding and implementation of complex statistical strategies.