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Related Experiment Videos

Extending McNemar's test: estimation and inference when paired binary outcome data are misclassified.

Robert H Lyles1, John M Williamson, Hung-Mo Lin

  • 1Department of Biostatistics, The Rollins School of Public Health of Emory University, 1518 Clifton Road N.E., Atlanta, Georgia 30322, USA. rlyles@sph.emory.edu

Biometrics
|March 2, 2005
PubMed
Summary

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McNemar's test is commonly used for paired binary data. This study introduces methods to estimate true differences and odds ratios using validation data, especially with imperfect diagnostic tests.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Diagnostic Test Evaluation

Background:

  • McNemar's test is widely used for paired binary outcomes in epidemiological studies.
  • Error-prone surrogate diagnostic tests are often employed due to cost and feasibility.
  • Accurate estimation of true differences and odds ratios is crucial when using surrogate tests.

Purpose of the Study:

  • To develop methods for estimating the true difference in proportions and the paired-data odds ratio.
  • To incorporate external or internal validation data when using imperfect surrogate diagnostic tests.
  • To address scenarios where diagnostic test properties may or may not remain constant over time.

Main Methods:

  • Likelihood-based analysis for point and confidence interval estimation.

Related Experiment Videos

  • Distinguishing between external and internal validation data.
  • Considering internal validation study designs for optimized efficiency.

    • Main Results:

    • Closed-form estimates are available for external validation data.
    • Numeric optimization is required for internal validation data, offering robustness and efficiency.
    • The methods are demonstrated using bacterial vaginosis diagnosis data.

    Conclusions:

    • The proposed methods enhance the accuracy of estimating treatment effects and diagnostic accuracy in epidemiological studies.
    • Internal validation data provide robust and efficient estimation of odds ratios.
    • These approaches are valuable for studies utilizing surrogate diagnostic tests with correlated binary outcomes.