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Related Experiment Videos

Estimating marginal and incremental effects on health outcomes using flexible link and variance function models.

Anirban Basu1, Paul J Rathouz

  • 1Section of General Internal Medicine, Department of Medicine, University of Chicago, 5841 South Maryland Ave-MC 2007, Chicago IL 60637, USA. abasu@medicine.bsd.uchicago.edu

Biostatistics (Oxford, England)
|December 25, 2004
PubMed
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This study introduces a new method for generalized linear models, allowing simultaneous estimation of regression coefficients, link function, and variance structure. This approach enhances inference on outcome means and covariate effects, proving consistent via simulations.

Area of Science:

  • Statistics
  • Econometrics
  • Biostatistics

Background:

  • Generalized linear models (GLMs) are widely used for analyzing various data types.
  • Traditional GLMs often assume a fixed link function and variance structure.
  • Accurate model specification is crucial for reliable inference on covariate effects.

Purpose of the Study:

  • To extend estimating equations in GLMs for simultaneous estimation of regression coefficients, link function, and variance structure.
  • To facilitate inference on the mean of the outcome as a function of covariates.
  • To define and estimate covariate effects, including an analogous parameter for discrete covariates.

Main Methods:

  • Developed an extension to estimating equations within the framework of generalized linear models.

Related Experiment Videos

  • Proposed a method for simultaneous estimation of parameters in the link function, variance structure, and regression coefficients.
  • Utilized Monte Carlo simulations to assess the consistency of the parameter estimators.

    • Main Results:

    • The proposed estimation method demonstrated consistency of parameter estimators in simulations.
    • The method aids in identifying appropriate link functions and suggesting underlying distributions.
    • It provides a robust estimation approach when the outcome distribution is uncertain.

    Conclusions:

    • The novel estimation method offers a flexible and robust approach to GLM analysis.
    • It improves the ability to perform accurate inference on covariate effects.
    • The method is applicable to diverse datasets, as illustrated with inpatient expenditure data.