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Related Experiment Videos

Test statistic and sample size for a two-sample McNemar test.

E J Feuer1, L G Kessler

  • 1National Cancer Institute, Division of Cancer Prevention and Control, Bethesda, Maryland 20892.

Biometrics
|June 1, 1989
PubMed
Summary
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This study generalizes McNemar's test for comparing marginal changes between two independent groups, crucial for intervention studies with binary outcomes. It simplifies sample size calculations, demonstrated with breast cancer screening utilization.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Trials

Background:

  • McNemar's test assesses marginal homogeneity in paired categorical data.
  • Comparing changes in binary outcomes between two independent cohorts (e.g., control vs. intervention) requires a generalized approach.
  • Accurate sample size calculation is vital for the statistical power of such comparative studies.

Purpose of the Study:

  • To generalize McNemar's test for comparing marginal changes between two independent samples.
  • To develop a method for sample size calculation in two-sample comparative studies with binary outcomes.
  • To illustrate the application of this method in a breast cancer screening utilization study.

Main Methods:

  • Generalization of McNemar's test to a two-sample setting.

Related Experiment Videos

  • Development of simplified sample size calculation formulas under realistic assumptions.
  • Application and demonstration using a real-world intervention study design.

    • Main Results:

    • A statistically sound method for comparing marginal changes in two independent cohorts was established.
    • Simplified sample size calculations are presented, facilitating study design.
    • The methodology is practically demonstrated for planning breast cancer screening intervention studies.

    Conclusions:

    • The generalized McNemar's test provides a robust framework for analyzing changes in binary outcomes across independent groups.
    • The derived sample size calculations enhance the efficiency and feasibility of intervention studies.
    • This approach is particularly valuable for public health interventions aiming to improve health behaviors like cancer screening.