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Regression models for multiple outcomes in large epidemiologic studies

S B Bull1

  • 1Samuel Lunenfeld Research Institute, University of Toronto, Canada. bull@mshri.on.ca

Statistics in Medicine
|November 5, 1998
PubMed
Summary
This summary is machine-generated.

Generalized estimating equations (GEE) models offer a flexible approach for analyzing multiple outcomes in regression, addressing challenges in epidemiologic studies with multiple endpoints. These methods provide robust variance estimates and handle multiplicity effectively.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Medical Statistics

Background:

  • Epidemiologic analyses often involve multiple outcomes and explanatory covariates.
  • Specifying a single primary outcome can be challenging in certain study designs.
  • Analyzing multiple outcomes requires methods that account for outcome dependence and multiplicity.

Purpose of the Study:

  • To compare alternative approaches for analyzing multiple outcomes in regression models.
  • To evaluate the utility of generalized estimating equations (GEE) for multivariate outcome analysis.
  • To address issues of multiplicity and robust variance estimation in epidemiologic studies.

Main Methods:

  • Utilized generalized estimating equations (GEE), a multivariate extension of generalized linear models.
  • Applied GEE model fitting with quasi-likelihood score and Wald tests in a hospital-population-based study.
  • Explored two GEE model specifications: one allowing differing outcome-covariate associations and another assuming a common association.
  • Incorporated simultaneous inference methods and empirical Bayes methods to address multiple inference.

Main Results:

  • GEE models successfully incorporated dependence among multiple outcomes from the same subject.
  • Robust variance estimates were obtained using the 'sandwich' variance estimator.
  • Different GEE model specifications provided distinct approaches to handling multiplicity, including global tests and shrinkage methods.
  • The study demonstrated the flexibility of GEE for estimating and testing associations with multiple outcomes.

Conclusions:

  • Generalized estimating equations (GEE) provide a flexible and robust framework for the analysis of multiple outcomes in regression.
  • GEE models effectively handle outcome dependence and offer strategies for addressing multiple inference in epidemiologic research.
  • The methods applied are valuable for studies with multiple correlated outcomes, such as complications of surgical anesthesia.