L1Penalized Estimation in the Cox Proportional Hazards Model
Copyright policy )L1Penalized Estimation in the Cox Proportional Hazards Model
AbstractThis article presents a novel algorithm that efficiently computesL1penalized (lasso) estimates of parameters in high‐dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high‐dimensional data. The new algorithm is based on a combination of gradient ascent optimization with the Newton–Raphson algorithm. It is described for a general likelihood function and can be applied in generalized linear models and other models with anL1penalty. The algorithm is demonstrated in the Cox proportional hazards model, predicting survival of breast cancer patients using gene expression data, and its performance is compared with competing approaches. AnRpackage,penalized, that implements the method, is available on CRAN.
- Leiden University Medical Center Netherlands
Numerical optimization and variational techniques, Generalized linear models (logistic models), penalty, Models, Genetic, Gene Expression Profiling, Computational problems in statistics, Gradient ascent Lasso Penalty Survival gene-expression data b-cell lymphoma logistic-regression breast-cancer variable selection predict survival lasso algorithm, Estimation in survival analysis and censored data, Breast Neoplasms, gradient ascent, survival, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, Medical applications (general), Humans, Computer Simulation, Female, lasso, Algorithms, Proportional Hazards Models
Numerical optimization and variational techniques, Generalized linear models (logistic models), penalty, Models, Genetic, Gene Expression Profiling, Computational problems in statistics, Gradient ascent Lasso Penalty Survival gene-expression data b-cell lymphoma logistic-regression breast-cancer variable selection predict survival lasso algorithm, Estimation in survival analysis and censored data, Breast Neoplasms, gradient ascent, survival, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, Medical applications (general), Humans, Computer Simulation, Female, lasso, Algorithms, Proportional Hazards Models
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