Article,
Semiparametric and nonparametric modeling for effect modification in matched studies
Computational Statistics & Data Analysis, 46 (4): 631-643 (July 2004)3621<m:linebreak></m:linebreak>Mesures d'associació.
DOI: 10.1016/j.csda.2003.09.002
Abstract
This study describes a new graphical method for assessing and characterizing effect modification by a matching covariate in matched case-control studies. This method to understand effect modification is based on a senniparametric model using a varying coefficient model. The method allows for nonparametric relationships between effect modification and other covariates, or can be useful in suggesting parametric models. This method can be applied to examining effect modification by any ordered categorical or continuous covariates for which cases have been matched with controls. The method applies to effect modification when causality might be reasonably assumed. An example from veterinary medicine is used to demonstrate our approach. The simulation results show that this method, when based on linear, quadratic and nonparametric effect modification, can be more powerful than both a parametric multiplicative model fit and a fully nonparametric generalized additive model fit. (C) 2003 Elsevier B.V. All rights reserved.
