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Related Experiment Videos

Analyzing correlated binary data using SAS.

S R Lipsitz1, D P Harrington

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115.

Computers and Biomedical Research, an International Journal
|June 1, 1990
PubMed
Summary
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Analyzing correlated repeated binary measurements requires careful variance estimation. Ordinary logistic regression provides consistent parameter estimates but requires adjustments for accurate variance calculation in statistical analysis.

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Repeated binary measurements on individuals are common in health research.
  • These measurements are inherently correlated, violating independence assumptions of standard models.
  • Ignoring correlation can lead to inaccurate statistical inference.

Purpose of the Study:

  • To present methods for analyzing correlated repeated binary measurements.
  • To address the issue of inconsistent variance estimates in ordinary logistic regression.
  • To provide practical guidance for obtaining accurate estimates in SAS.

Main Methods:

  • Utilizing ordinary logistic regression for parameter estimation.
  • Implementing robust variance estimation techniques for correlated data.

Related Experiment Videos

  • Describing SAS procedures for consistent covariance matrix estimation.

    • Main Results:

    • Ordinary logistic regression maximum likelihood estimates are consistent and asymptotically normal despite correlation.
    • The standard information matrix inverse yields inconsistent variance estimates.
    • A consistent estimate of the covariance matrix can be obtained with minimal manipulation.

    Conclusions:

    • Consistent parameter estimates are achievable with standard logistic regression.
    • Accurate variance estimation is crucial for valid inference with repeated measures.
    • The described SAS methods facilitate reliable analysis of correlated binary data.