Affiliation:
1. Chair of Epidemiology, Department of Sport and Health Sciences Technical University of Munich Munich Germany
2. Institute of Biomedical Statistics, Computer Science and Epidemiology University of Bonn Bonn Germany
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.
Funder
Deutsche Forschungsgemeinschaft
Subject
Statistics and Probability,Epidemiology