• Corpus ID: 249017545

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

@inproceedings{Guo2022ASA,
  title={A scalable and flexible Cox proportional hazards model for high-dimensional survival prediction and functional selection},
  author={Boyi Guo and Nengjun Yi},
  year={2022}
}
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… 

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

References

SHOWING 1-10 OF 49 REFERENCES
REGULARIZATION FOR COX'S PROPORTIONAL HAZARDS MODEL WITH NP-DIMENSIONALITY.
TLDR
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 Wu, D. 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
TLDR
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
TLDR
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
TLDR
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
Group spike‐and‐slab lasso generalized linear models for disease prediction and associated genes detection by incorporating pathway information
TLDR
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.
Adaptive Lasso for Cox's proportional hazards model
We investigate the variable selection problem for Cox's proportional hazards model, and propose a unified model selection and estimation procedure with desired theoretical properties and
Additive Hazards Regression Models for Survival Data
The additive hazards regression model relates the conditional hazard function of the failure time linearly to the covariates. This formulation complements the familiar proportional hazards model in
Model selection in nonparametric hazard regression
We propose a novel model selection method for a nonparametric extension of the Cox proportional hazard model, in the framework of smoothing splines ANOVA models. The method automates the model
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