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Alternating logistic regressions with improved finite sample properties.

Jamie Perin1, John S Preisser2

  • 1Department of International Health, Johns Hopkins University, Baltimore, Maryland, U.S.A.

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Summary

New statistical methods improve analysis of correlated data in cluster trials. Finite sample adjustments reduce bias in log odds ratio estimation, enhancing accuracy for correlated binary outcomes.

Keywords:
Cluster randomized trialsDental cariesGeneralized Estimating EquationsMarginal association modelingSmall samplesUnderage drinking

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Area of Science:

  • Biostatistics
  • Statistical modeling
  • Correlated data analysis

Background:

  • Alternating logistic regressions (ALR) model marginal means of correlated binary outcomes.
  • ALR specifies within-cluster association models using log odds ratios.
  • A generalization, orthogonalized residuals, exists for ALR.

Purpose of the Study:

  • Extend orthogonalized residuals for ALR to include finite sample adjustments.
  • Improve estimation of log odds ratio model parameters with a small number of clusters.
  • Reduce bias in variance estimators and association parameter equations.

Main Methods:

  • Developed finite sample adjustments for orthogonalized residuals in ALR.
  • Applied bias adjustments to sandwich variance estimators.
  • Incorporated bias adjustments into estimating equations for association parameters.

Main Results:

  • Demonstrated methods in cluster trials for reducing underage drinking and analyzing dental caries incidence.
  • Simulation studies showed improved performance (bias and coverage) compared to uncorrected methods.
  • Proposed adjustments enhance the reliability of statistical inference in cluster randomized trials.

Conclusions:

  • Finite sample adjustments significantly improve the accuracy of log odds ratio estimation in ALR.
  • The enhanced methods provide more reliable results for correlated binary outcomes in cluster trials.
  • These advancements are crucial for robust statistical analysis in public health and epidemiological research.