Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events
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Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events. / Ojeda, Francisco M; Müller, Christian; Börnigen, Daniela; Trégouët, David-Alexandre; Schillert, Arne; Heinig, Matthias; Zeller, Tanja; Schnabel, Renate B.
in: GENOM PROTEOM BIOINF, Jahrgang 14, Nr. 4, 08.2016, S. 235-343.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Comparison of Cox Model Methods in A Low-dimensional Setting with Few Events
AU - Ojeda, Francisco M
AU - Müller, Christian
AU - Börnigen, Daniela
AU - Trégouët, David-Alexandre
AU - Schillert, Arne
AU - Heinig, Matthias
AU - Zeller, Tanja
AU - Schnabel, Renate B
N1 - Copyright © 2016 The Authors. Production and hosting by Elsevier Ltd.. All rights reserved.
PY - 2016/8
Y1 - 2016/8
N2 - 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.
AB - 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.
KW - Biomarkers
KW - Coronary Artery Disease
KW - Genetic Variation
KW - Humans
KW - Prognosis
KW - Proportional Hazards Models
KW - Prospective Studies
KW - Journal Article
KW - Research Support, Non-U.S. Gov't
U2 - 10.1016/j.gpb.2016.03.006
DO - 10.1016/j.gpb.2016.03.006
M3 - SCORING: Journal article
C2 - 27224515
VL - 14
SP - 235
EP - 343
JO - GENOM PROTEOM BIOINF
JF - GENOM PROTEOM BIOINF
SN - 1672-0229
IS - 4
ER -