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Efficient experimental design for binary matched pairs data.

Kristin P Lennox1, Michael Sherman

  • 1Department of Statistics, Texas A&M University, College Station, Texas 77843, USA. lennox@stat.tamu.edu

Statistics in Medicine
|August 20, 2009
PubMed
Summary
This summary is machine-generated.

McNemar's test is recommended for matched pairs binary experiments. Analyzing power and size across all discordance probabilities ensures robust experimental design and accurate hypothesis testing for various applications.

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Area of Science:

  • Biostatistics
  • Statistical Methods
  • Experimental Design

Background:

  • Matched pairs binary experiments are common in various scientific fields.
  • McNemar's test is frequently used for analyzing such data.
  • Existing methods for determining sample size and power have limitations due to ignoring variability in the discordance probability (p).

Purpose of the Study:

  • To address the limitations of current sample size and power calculation methods for matched pairs binary experiments.
  • To recommend a comprehensive approach for evaluating statistical tests by considering the full range of the discordance probability.
  • To compare the performance of McNemar's test and its variants.

Main Methods:

  • Developed and analyzed exact power and size functions for hypothesis tests used in matched pairs binary experiments.
  • Evaluated test performance across the entire spectrum of possible discordance probability values.
  • Applied the methodology to real-world datasets, including genetic linkage analysis and pain management studies.

Main Results:

  • McNemar's test generally demonstrates the closest adherence to nominal size and exhibits the highest power among common variants.
  • Viewing size and power functions across all discordance probabilities provides a complete understanding of test behavior.
  • The proposed method allows for effective comparison of different hypothesis tests.

Conclusions:

  • A comprehensive analysis of size and power functions across all discordance probabilities is crucial for robust experimental design in matched pairs binary experiments.
  • McNemar's test is often the preferred choice due to its favorable statistical properties.
  • The demonstrated technique is applicable to diverse research areas, enhancing the reliability of statistical inference.