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Related Experiment Videos

The analysis of binary and categorical data from crossover trials

M G Kenward1, B Jones

  • 1Statistics Department, Rothamsted Experimental Station, BBSRC Institute of Arable Crops Research, Harpenden, Hertfordshire, UK.

Statistical Methods in Medical Research
|December 1, 1994
PubMed
Summary

This review covers methods for analyzing discrete data in crossover trials, focusing on model interpretation. Recent techniques for correlated categorical data analysis are applicable and compared using examples.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Crossover trials generate discrete data, requiring specialized analytical methods.
  • Understanding the underlying data model is crucial for accurate interpretation.
  • Existing methods for correlated categorical data may be adapted for crossover designs.

Purpose of the Study:

  • To review and synthesize methods for analyzing discrete data in crossover trials.
  • To clarify the definition and interpretation of statistical models for such data.
  • To assess the applicability and accessibility of recent analytical techniques.

Main Methods:

  • Literature review of statistical methods for discrete data analysis.
  • Focus on marginal and subject-specific model distinctions.

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  • Application and comparison of methods using illustrative examples.

    • Main Results:

    • Recent methodologies for correlated categorical data analysis are adaptable to crossover trials.
    • Different analytical approaches show varying degrees of accessibility.
    • Two examples demonstrate the practical application and comparison of methods.

    Conclusions:

    • Adaptation of correlated categorical data methods offers viable solutions for crossover trial analysis.
    • Model definition and interpretation are key considerations.
    • Comparative analysis aids in selecting appropriate methods for discrete crossover trial data.