• Corpus ID: 249017545

A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection

  title={A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection},
  author={Boyi Guo and Nengjun Yi},
Cox proportional hazards model is one of the most popular models in biomedical data analysis. There have been continuing efforts to improve the flexibility of such models for complex signal detection, for example, via additive functions. Nevertheless, the task to extend Cox additive models to accomodate high-dimensional data is nontrivial. When estimating additive functions, commonly used group sparse regularization may introduce excess smoothing shrinkage on additive functions, damaging… 

Figures and Tables from this paper

The R Package BHAM: Fast and Scalable Bayesian Hierarchical Additive Model for High-dimensional Data

The models, algorithms and related features implemented in BHAM are described and the package is freely available via the public GitHub repository https://github.com/boyiguo1/BHAM.



Additive Functional Cox Model

Abstract We propose the additive functional Cox model to flexibly quantify the association between functional covariates and time to event data. The model extends the linear functional proportional


Strong oracle properties of non-concave penalized methods for non-polynomial (NP) dimensional data with censoring in the framework of Cox's proportional hazards model are established.

Flexible and Interpretable Models for Survival Data

  • Jiacheng WuD. Witten
  • Computer Science
    Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
  • 2019
An additive Cox proportional hazards model is developed, in which each additive function is obtained by trend filtering, so that the fitted functions are piece-wise polynomial with adaptively-chosen knots.

The spike‐and‐slab lasso Cox model for survival prediction and associated genes detection

This work proposes new Bayesian hierarchical Cox proportional hazards models, called the spike‐and‐slab lasso Cox, for predicting survival outcomes and detecting associated genes and develops an efficient algorithm to fit the proposed models by incorporating Expectation‐Maximization steps into the extremely fast cyclic coordinate descent algorithm.

High-dimensional variable selection for Cox's proportional hazards model

This work extends the sure screening procedure to Cox's proportional hazards model with an iterative version available and demonstrates the utility and versatility of the iterative sure independence screening scheme.

Variable Selection for Cox's proportional Hazards Model and Frailty Model

A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed in Fan and Li (2001a). It has been shown there that the resulting procedures perform as

Gsslasso Cox: a Bayesian hierarchical model for predicting survival and detecting associated genes by incorporating pathway information

A Bayesian hierarchical Cox survival model, called the group spike-and-slab lasso Cox (gsslasso Cox), for predicting disease survival outcomes and detecting associated genes by incorporating group structures of biological pathways by employing a novel prior on the coefficients of genes.

Feature screening in ultrahigh-dimensional additive Cox model

The proposed screening procedure for the additive Cox model with ultrahigh-dimensional covariates can effectively identify active predictors and it is proved that the proposed procedure possesses the sure screening property.

Group spike‐and‐slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information

A fast and stable deterministic algorithm is developed to fit the proposed hierarchal GLMs, called group spike‐and‐slab lassoGLMs, for predicting disease outcomes and detecting associated genes by incorporating large‐scale molecular data and group structures.


A penalized partial likelihood procedure is proposed to simultaneously estimate the parameters and select variables for both the parametric and the nonparametric parts of the Cox models with semiparametric relative risk, and it is shown that the resulting estimator of theparametric part possesses the oracle property, and that the estimators achieves the optimal rate of convergence.