The effects of covariate adjustment in generalized linear models
Document Type
Article
Publication Date
1-1-1998
Abstract
Results from classical linear regression regarding the effects of covariate adjustment, with respect to the issues of confounding, the precision with which an exposure effect can be estimated, and the efficiency of hypothesis tests for no treatment effect in randomized experiments, are often assumed to apply more generally to other types of regression models. In this paper results pertaining to several generalized linear models involving a dichotomous response variable are given, demonstrating that with respect to the issues of confounding and precision, for models having a linear or log link function the results of classical linear regression do generally apply, whereas for other models, including those having a logit, probit, log-log, complementary log-log, or generalized logistic link function, the results of classical linear regression do not always apply. It is also shown, however, that for any link function, covariate adjustment results in improved efficiency of hypothesis tests for no treatment effect in randomized experiments, and hence that the classical linear regression results regarding efficiency do apply for all models having a dichotomous response variable. Copyright © 1998 by Marcel Dekker, Inc.
Publication Title
Communications in Statistics - Theory and Methods
Recommended Citation
Robinson, L.,
Robert Dorroh, J.,
Lien, D.,
&
Tiku, M.
(1998).
The effects of covariate adjustment in generalized linear models.
Communications in Statistics - Theory and Methods,
27(7), 1653-1675.
http://doi.org/10.1080/03610929808832183
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/9236