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Related Experiment Videos

Design effects for binary regression models fitted to dependent data

J M Neuhaus1, M R Segal

  • 1Division of Biostatistics, University of California, San Francisco 94143-0560.

Statistics in Medicine
|July 15, 1993
PubMed
Summary
This summary is machine-generated.

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Cluster sampling inflates variance, increasing parameter estimate uncertainty. This study shows linear regression methods accurately approximate design effects for generalized linear models, aiding statistical analysis.

Area of Science:

  • Statistics
  • Biostatistics
  • Survey Methodology

Background:

  • Dependent data, common in cluster sampling, inflates variance compared to simple random sampling.
  • The variance inflation factor is termed the design effect.
  • Design effects are established for simple estimators and linear regression coefficients.

Purpose of the Study:

  • To extend design effect derivation methods from linear regression to generalized linear models for binary responses.
  • To assess the accuracy of linear regression-based design effect approximations for binary regression models.

Main Methods:

  • Extending existing methods for calculating design effects in linear regression.
  • Applying these methods to generalized linear models with binary outcomes.

Related Experiment Videos

  • Utilizing logistic, probit, and complementary log-log link functions.
  • Conducting simulation studies and analyzing real-world examples.

    • Main Results:

    • The method for deriving design effects in linear regression successfully extends to generalized linear models for binary data.
    • Simple expressions for design effects from linear regression provide accurate approximations for binary regression models.
    • Validation through two case studies and simulation analyses.

    Conclusions:

    • The proposed method offers a practical approach to estimating design effects in complex binary data settings.
    • Linear regression approximations are reliable for generalized linear models, improving variance estimation in cluster sampling.
    • Findings support more accurate statistical inference in studies employing cluster sampling for binary outcomes.