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Related Concept Videos

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...
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Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to...
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Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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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.
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The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
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One-Way ANOVA: Unequal Sample Sizes

One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:

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A Within-Subject Experimental Design using an Object Location Task in Rats
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Multiple contrasts for repeated measures.

Mario Hasler1

  • 1Christian-Albrechts-University, Kiel, Germany. hasler@email.uni-kiel.de

The International Journal of Biostatistics
|July 31, 2013
PubMed
Summary

This study compares methods for multiple contrast tests in repeated measures data. Sandwich estimators offer the most robust familywise error rate control for these statistical tests.

Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Repeated measures designs are common in scientific research.
  • Controlling for multiple comparisons is crucial to avoid inflated Type I errors.
  • Existing methods for multiple contrast tests in repeated measures may lack robustness.

Purpose of the Study:

  • To evaluate and compare three procedures for multiple contrast tests in repeated measures.
  • To assess the familywise error rate (FWER) Type I performance of these procedures.
  • To identify the most robust procedure for various comparison scenarios.

Main Methods:

  • The study employed Monte Carlo simulations to compare statistical procedures.
  • Familywise error rate (FWER) Type I was the primary metric for comparison.

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  • Three distinct procedures for multiple contrast tests were simulated and analyzed.

    • Main Results:

    • The procedure utilizing sandwich estimators demonstrated superior robustness in controlling the familywise error rate.
    • This robustness was observed across various simulation conditions.
    • An exception was noted for all-pair comparisons, where performance varied.

    Conclusions:

    • Sandwich estimators provide a robust approach for multiple contrast tests in repeated measures.
    • Researchers should consider sandwich estimators for reliable statistical inference.
    • Careful consideration is needed when performing all-pair comparisons in such designs.