Schauberger Gunther, Tanaka Luana Fiengo, Berger Moritz
Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany.
Institute of Biomedical Statistics, Computer Science and Epidemiology, University of Bonn, Bonn, Germany.
Stat Med. 2023 Feb 28;42(5):676-692. doi: 10.1002/sim.9637. Epub 2023 Jan 11.
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.
条件逻辑回归（CLR）是分析配对病例对照研究无可争议的标准方法。然而，CLR在纳入混杂变量的非线性效应和相互作用方面受到很大限制。本文提出了一种基于树的新型建模方法，该方法解决了这一问题，并提供了一个灵活的框架，允许更复杂的混杂结构。所提出的机器学习模型是在CLR框架内拟合的，因此可以考虑数据中的配对分层。模拟研究证明了该方法的有效性。此外，为了说明，该方法应用于一项关于宫颈癌的配对病例对照研究。
