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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

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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.
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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.
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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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A complete procedure for testing a claim about a population proportion is provided here.
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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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Sample size planning for multiple contrast tests.

Anna Pöhlmann1, Frank Konietschke1

  • 1Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Biometry and Clinical Epidemiology, Berlin, Germany.

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Summary
This summary is machine-generated.

This study introduces new sample size calculation methods for multiple-sample preclinical studies using multiple contrast tests. These accurate, accessible tools improve the planning of clinical trials with several samples.

Keywords:
multiple contrast testnonparametric procedurepower considerationssample size determinationsteel test

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Area of Science:

  • Biostatistics
  • Preclinical Research
  • Clinical Trial Design

Background:

  • Established sample size calculations exist for two independent samples in clinical research.
  • Planning sample sizes for multiple samples, common in preclinical studies, presents challenges.
  • Existing methods often use analysis of variance (ANOVA) with difficult-to-interpret effect sizes.

Purpose of the Study:

  • To develop and validate sample size calculation methods for multiple samples in preclinical research.
  • To employ multiple contrast test procedures for more interpretable sample size planning.
  • To provide accurate and accessible tools for clinical trial design with several samples.

Main Methods:

  • Utilized multiple contrast test procedures for sample size computations.
  • Applied methods to both parametric (normality assumption) and nonparametric designs using Steel-type tests.
  • Employed approximate solutions and numerical approximations due to unknown distributions and lack of closed formulas.

Main Results:

  • Developed accurate sample size calculation methods for multiple samples.
  • Simulation studies confirmed that the methods achieve the target power for detecting alternatives.
  • The proposed procedures are effective for planning preclinical and clinical trials with multiple samples.

Conclusions:

  • The developed multiple contrast test-based sample size calculations are accurate and reliable.
  • These methods offer a valuable tool for researchers planning preclinical and clinical trials with several samples.
  • The procedures are accessible via publicly available software, facilitating their widespread adoption.