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Biostatistics 2005 6(1):93-109; doi:10.1093/biostatistics/kxh020
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Biostatistics Vol. 6 No. 1 © Oxford University Press 2005; all rights reserved.

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

Anirban Basu*

Section of General Internal Medicine, Department of Medicine, University of Chicago, 5841 South Maryland Ave—MC 2007, Chicago IL 60637, USA abasu{at}medicine.bsd.uchicago.edu

Paul J. Rathouz

Department of Health Studies, University of Chicago, 5841 South Maryland Ave—MC 2007, Chicago IL 60637, USA prathouz{at}health.bsd.uchicago.edu

* To whom correspondence should be addressed.

We propose an extension to the estimating equations in generalized linear models to estimate parameters in the link function and variance structure simultaneously with regression coefficients. Rather than focusing on the regression coefficients, the purpose of these models is inference about the mean of the outcome as a function of a set of covariates, and various functionals of the mean function used to measure the effects of the covariates. A commonly used functional in econometrics, referred to as the marginal effect, is the partial derivative of the mean function with respect to any covariate, averaged over the empirical distribution of covariates in the model. We define an analogous parameter for discrete covariates. The proposed estimation method not only helps to identify an appropriate link function and to suggest an underlying distribution for a specific application but also serves as a robust estimator when no specific distribution for the outcome measure can be identified. Using Monte Carlo simulations, we show that the resulting parameter estimators are consistent. The method is illustrated with an analysis of inpatient expenditure data from a study of hospitalists.

Keywords: Econometric models; Estimating equations; Generalized linear models; Link function; Variance function


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