Damla Sentürk
Statistics Department
University of California
Covariate Adjusted Regression
We introduce covariate adjusted regression (CAR) for situations where both
predictors and response in a regression model are not directly observable,
but are contaminated with a multiplicative factor that is determined by the
value of an unknown function of an observable covariate. We demonstrate how
the regression coefficients can be estimated by establishing a connection to
varying coefficient regression. The proposed covariate adjustment method is
illustrated with an analysis of the regression of plasma fibrinogen
concentration as response on serum transferrin level as predictor for 69
hemodialysis patients. In this example, both response and predictor are
thought to be in influenced in a multiplicative fashion by body mass index.
A bootstrap hypothesis test enables us to test the significance of the
regression parameters. We establish the asymptotic distribution of the
parameter estimates for this new covariate adjusted regression model.
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