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Estimating marginal proportions and intraclass correlations with clustered binary data.

Josep L Carrasco1, Yi Pan2, Rosa Abellana1,3

  • 1Biostatistics, Department of Basic Clinical Practice, University of Barcelona, Barcelona, Spain.

Biometrical Journal. Biometrische Zeitschrift
|December 12, 2018
PubMed
Summary
This summary is machine-generated.

Estimating marginal success proportion and intraclass correlation from clustered binary data is challenging with random effects logistic regression. Linear mixed models (LMM) and generalized estimating equations (GEE) offer reliable approximations, especially with large sample sizes.

Keywords:
generalized linear-mixed modelintraclass correlationlogistic regressionmarginal proportionrandom effects

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Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Logistic regression with random effects is standard for clustered binary data, assuming varying cluster success proportions.
  • Assessing marginal success proportion and intraclass correlation is crucial but analytically challenging with this model.
  • Existing methods lack clear analytical expressions for marginal proportion and intraclass correlation estimators.

Purpose of the Study:

  • To compare different approximation approaches for analyzing clustered binary data.
  • To evaluate the performance of logistic-normal (LN) mixed effects models, linear mixed models (LMM), and generalized estimating equations (GEE).
  • To identify reliable methods for estimating marginal proportion and intraclass correlation.

Main Methods:

  • Comparative analysis of LN, LMM, and GEE approaches.
  • Utilized two real-world datasets for empirical evaluation.
  • Conducted a simulation study to assess performance under varying conditions.

Main Results:

  • The performance of all methods is sensitive to the marginal proportion, intraclass correlation, and sample size.
  • Reliability generally decreases with lower marginal proportions and higher intraclass correlations.
  • LMM and GEE demonstrated reliability, particularly in scenarios with large sample sizes.

Conclusions:

  • The choice of method for analyzing clustered binary data impacts the accuracy of marginal proportion and intraclass correlation estimates.
  • Linear mixed models and generalized estimating equations are robust alternatives, especially for large datasets.
  • Careful consideration of data characteristics (proportion, correlation, sample size) is essential for selecting an appropriate analytical approach.