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Sample size and power for McNemar's test with clustered data.

Mithat Gönen1

  • 1Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY 10021, U.S.A. gonenm@mskcc.org

Statistics in Medicine
|July 6, 2004
PubMed
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This study introduces two approximations for sample size calculations in clustered binary data using an adjusted McNemar's test. These methods ensure statistical validity for paired binary comparisons in complex datasets.

Area of Science:

  • Biostatistics
  • Statistical Methods
  • Medical Imaging Analysis

Background:

  • McNemar's test is standard for paired binary data.
  • Clustered data requires adjustments to McNemar's test for validity.
  • Accurate sample size calculation is crucial for study design.

Purpose of the Study:

  • To present two novel approximations for power and sample size calculations.
  • To address the adjusted McNemar's test for clustered binary data.
  • To evaluate the accuracy of these approximations via simulation.

Main Methods:

  • Developed two approximation methods for sample size and power.
  • Conducted a simulation study to validate the approximations.
  • Applied the methodology to a positron emission tomography (PET) study for colorectal cancer detection.

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Main Results:

  • The simulation study demonstrated the accuracy of the proposed approximations.
  • The methods provide reliable sample size computations for adjusted McNemar's test.
  • Sensitivity analyses explored the impact of design parameters on sample size.

Conclusions:

  • The presented approximations are accurate and useful for planning studies with clustered binary data.
  • The methodology facilitates robust study design in areas like medical imaging.
  • Accurate sample size determination is essential for detecting effects in clustered binary outcome studies.