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Assessing Binocular Central Visual Field and Binocular Eye Movements in a Dichoptic Viewing Condition
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Tutorial on Biostatistics: Statistical Analysis for Correlated Binary Eye Data.

Gui-Shuang Ying1, Maureen G Maguire1, Robert Glynn2

  • 1a Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology , Perelman School of Medicine, University of Pennsylvania , Philadelphia , PA , USA.

Ophthalmic Epidemiology
|May 24, 2017
PubMed
Summary
This summary is machine-generated.

Analyzing correlated binary eye data requires accounting for inter-eye correlation. Ignoring this can lead to inaccurate statistical significance, while marginal or mixed-effects models provide valid inference for eye-related studies.

Keywords:
Correlated binary datageneralized estimating equationsgeneralized linear mixed effects modelinter-eye correlationmarginal model

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Area of Science:

  • Ophthalmology
  • Biostatistics
  • Clinical Trials

Background:

  • Ophthalmic research frequently involves binary outcomes measured on both eyes of a patient.
  • The correlation between eyes within an individual can impact statistical analyses if not properly addressed.

Purpose of the Study:

  • To describe and demonstrate statistical methods for analyzing correlated binary eye data.
  • To highlight the impact of inter-eye correlation on statistical inference in ophthalmic studies.

Main Methods:

  • Application of non-model-based methods (McNemar's test, Cochran-Mantel-Haenszel test).
  • Application of model-based methods (generalized linear mixed effects model, marginal model).
  • Analysis of data from three distinct clinical trials: CAPT, ETROP, and AREDS.

Main Results:

  • Ignoring inter-eye correlation led to non-significant results in CAPT, which became significant when correlation was accounted for.
  • Standard logistic regression in ETROP produced different standard errors and p-values compared to models that included inter-eye correlation.
  • Two-eye analyses in AREDS, accounting for inter-eye correlation, offered greater power than one-eye analyses.

Conclusions:

  • Failure to account for inter-eye correlation can distort statistical significance (p-values).
  • Marginal models and mixed-effects models, treating the eye as the unit of analysis, are recommended for valid inference in studies with correlated eye data.