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Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events

Genomics, Proteomics & Bioinformatics 2016年第4期14卷 235-243页

作者： Francisco M. Ojeda Christian Miiller Daniela Bornigen David-Alexandre Tregouet Arne Schillert Matthias Heinig Tanja Zeller Renate B. Schnabel Department of General and Interventional Cardiology University Heart Center Hamburg-Eppendorf 20246 Hamburg Germany German Center for Cardiovascular Research (DZHK) Hamburg |Kiel| Luebeck Germany Sorbonne Universites University Pierre et Marie Curie Paris 06 Institut National pour la Sante et la Recherche Medicale (INSERM) Unite Mixte de Recherche en Sante (UMR_S) 1166 F-75013 Paris France Institute for Cardiometaholism and Nutrition (ICAN) F-75013 Paris France Institut fur Medizinische Biometrie und Statistik Universitat zu Lubeck Universitatsklinikum Schleswig-Holstein Campus Lubeck 23562 Lubeck Germany Institute of Computational Biology German Research Center for Environmental Health Helmholtz Zentrum Munchen 85764 Neuherberg Germany

Prognostic models based on survival data frequently make use of the Cox proportional hazards model. Developing reliable Cox models with few events relative to the number of predictors can be challenging, even in low-dimensional datasets, with a much larger number of observations than variables. In such a setting we examined the performance of methods used to estimate a Cox model, including （i） full model using all available predictors and estimated by standard techniques, （ii） backward elimination （BE）, （iii） ridge regression, （iv） least absolute shrinkage and selection operator （lasso）, and （v） elastic net. Based on a prospective cohort of patients with manifest coronary artery disease （CAD）, we performed a simulation study to compare the predictive accuracy, calibration, and discrimination of these approaches, Candidate predictors for incident cardiovascular events we used included clinical variables, biomarkers, and a selection of genetic variants associated with CAD. The penalized methods, i.e., ridge, lasso, and elastic net, showed a comparable performance, in terms of predictive accuracy, calibration, and discrimination, and outperformed BE and the full model. Excessive shrinkage was observed in some cases for the penalized methods, mostly on the simulation scenarios having the lowest ratio of a number of events to the number of variables. We conclude that in similar settings, these three penalized methods can be used interchangeably. The full model and backward elimination are not recommended in rare event scenarios.

关键词： proportional hazards regression Penalized regression Events per variable Coronary artery disease

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