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Three variable selection techniques of Lasso, SCAD, and SICA were implemented on microarray gene expression data of 86 leukoplakia samples of OPL patients based on additive hazards model.
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Despite the extensive study of the proportional hazards model for survival data with high-dimensional covariates, a few authors have utilized variable selection methods based on additive risk model which may provide more insights beyond the proportional hazards model analysis [ 4, 13].
SVMs based on additive kernels will be called additive support vector machines.
The sapphire substrates were coated by immersion in the formulation based on Additive G50.
All p-values are based on additive models for the genetic variants.
The research in [ 17] is based on additive noise model.
Analyses based on additive and dominant models gave similar results (Supplementary Materials 1 8).
Again, a combined prediction considering additive and dominance effects was only slightly superior to predicting Septoria tritici blotch resistance based on additive effects only.
In addition, Lin and Lv [35] showed similar performance of Lasso and elastic net penalties based on the additive hazards model with the simulation data.
In this study, the performance of three sparse variable selection techniques was investigated based on an additive hazards model for survival data, in the presence of high-dimensional covariates.
The hazard function of a failure time T given a p-vector of possibly time-dependent covariates Z based on Lin and Ying additive hazards model is as follows: (1) λ (t ; Z ) = λ 0 (t ) + β 0 T Z, where λ0 is an unknown baseline hazard function which is common to all subjects and β0 is a p-vector of regression coefficients [ 4, 15].
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