High-Dimensional Cox Models: The Choice of Penalty as Part of the Model Building Process

DOI10.1002/BIMJ.200900064zbMath1442.62257OpenAlexW2049123275WikidataQ45054121 ScholiaQ45054121MaRDI QIDQ2786152

Axel Benner, Ulrich Mansmann, Thomas Hielscher, Manuela Zucknick, Carina Ittrich

Publication date: 21 September 2010

Published in: Biometrical Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/bimj.200900064

zbMATH Keywords

model selectionsurvival analysishigh dimensionspredictive accuracypenalized proportional hazards

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Estimation in survival analysis and censored data (62N02)

Related Items (9)

Bayesian accelerated failure time models based on penalized mixtures of Gaussians: regularization and variable selection ⋮ Comments on: ``Hierarchical inference for genome-wide association studies ⋮ Bayesian regularization for flexible baseline hazard functions in Cox survival models ⋮ An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models ⋮ Computational Statistics Solutions for Molecular Biomedical Research: A Challenge and Chance for Both ⋮ Use of pretransformation to cope with extreme values in important candidate features ⋮ Including network knowledge into Cox regression models for biomarker signature discovery ⋮ Hans van Houwelingen, 40 Years in Biostatistics ⋮ Shrinkage variable selection and estimation in proportional hazards models with additive structure and high dimensionality

Uses Software

• penalized
• uniCox

Cites Work

• Unnamed Item
• The Adaptive Lasso and Its Oracle Properties
• Survival prediction using gene expression data: a review and comparison
• Asymptotics for Lasso-type estimators.
• Variable selection for Cox's proportional hazards model and frailty model
• Simultaneous analysis of Lasso and Dantzig selector
• High-dimensional graphs and variable selection with the Lasso
• L₁Penalized Estimation in the Cox Proportional Hazards Model
• Univariate Shrinkage in the Cox Model for High Dimensional Data
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Regularization and Variable Selection Via the Elastic Net
• Adaptive Lasso for Cox's proportional hazards model
• Prediction by Supervised Principal Components

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