Research & Innovation
Publications
A tree-based modeling approach for matched case-control studies
PMU Author
Luana Fiengo Tanaka
All Authors
Gunther Schauberger, Luana Fiengo Tanaka, Moritz Berger
Journal association
STATISTICS IN MEDICINE
Abstract
Conditional logistic regression (CLR) is the indisputable standard method for the analysis of matched case-control studies. However, CLR is strongly restricted with respect to the inclusion of non-linear effects and interactions of confounding variables. A novel tree-based modeling method is proposed which accounts for this issue and provides a flexible framework allowing for a more complex confounding structure. The proposed machine learning model is fitted within the framework of CLR and, therefore, allows to account for the matched strata in the data. A simulation study demonstrates the efficacy of the method. Furthermore, for illustration the method is applied to a matched case-control study on cervical cancer.
Keywords
Cart, Conditional inference trees, Conditional logistic regression, Matched case-control studies, Matched pairs