Grouped variable selection in high dimensional partially linear additive Cox model

2010 ◽  
Author(s):  
Li Liu
Keyword(s):  
Variable Selection ◽  
Cox Model ◽  
High Dimensional ◽  
Partially Linear

scholarly journals Ensembling Variable Selectors by Stability Selection for the Cox Model

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Qing-Yan Yin ◽  
Jun-Li Li ◽  
Chun-Xia Zhang
Keyword(s):  
Variable Selection ◽  
Regression Models ◽  
Cox Model ◽  
High Dimensional ◽  
Linear Regression Models ◽  
Real World Data ◽  
Stability Selection ◽  
False Discovery ◽  
Selection For ◽  
Base Learner

As a pivotal tool to build interpretive models, variable selection plays an increasingly important role in high-dimensional data analysis. In recent years, variable selection ensembles (VSEs) have gained much interest due to their many advantages. Stability selection (Meinshausen and Bühlmann, 2010), a VSE technique based on subsampling in combination with a base algorithm like lasso, is an effective method to control false discovery rate (FDR) and to improve selection accuracy in linear regression models. By adopting lasso as a base learner, we attempt to extend stability selection to handle variable selection problems in a Cox model. According to our experience, it is crucial to set the regularization region Λ in lasso and the parameter λmin properly so that stability selection can work well. To the best of our knowledge, however, there is no literature addressing this problem in an explicit way. Therefore, we first provide a detailed procedure to specify Λ and λmin. Then, some simulated and real-world data with various censoring rates are used to examine how well stability selection performs. It is also compared with several other variable selection approaches. Experimental results demonstrate that it achieves better or competitive performance in comparison with several other popular techniques.

Variable selection for high-dimensional quadratic Cox model with application to Alzheimer’s disease

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Cong Li ◽  
Jianguo Sun
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Variable Selection ◽  
Linear Models ◽  
Cox Model ◽  
Partial Likelihood ◽  
High Dimensional ◽  
Stat Assoc ◽  
Selection For ◽  
Disease Study

AbstractThis paper discusses variable or covariate selection for high-dimensional quadratic Cox model. Although many variable selection methods have been developed for standard Cox model or high-dimensional standard Cox model, most of them cannot be directly applied since they cannot take into account the important and existing hierarchical model structure. For the problem, we present a penalized log partial likelihood-based approach and in particular, generalize the regularization algorithm under marginality principle (RAMP) proposed in Hao et al. (J Am Stat Assoc 2018;113:615–25) under the context of linear models. An extensive simulation study is conducted and suggests that the presented method works well in practical situations. It is then applied to an Alzheimer’s Disease study that motivated this investigation.

scholarly journals High-Dimensional Cox Regression Analysis in Genetic Studies with Censored Survival Outcomes

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Jinfeng Xu
Keyword(s):  
Relative Risk ◽  
Variable Selection ◽  
Cox Regression ◽  
Cox Model ◽  
Partial Likelihood ◽  
Likelihood Method ◽  
Dimensional Case ◽  
High Dimensional ◽  
Cox Regression Analysis ◽  
Penalization Methods

With the advancement of high-throughput technologies, nowadays high-dimensional genomic and proteomic data are easy to obtain and have become ever increasingly important in unveiling the complex etiology of many diseases. While relating a large number of factors to a survival outcome through the Cox relative risk model, various techniques have been proposed in the literature. We review some recently developed methods for such analysis. For high-dimensional variable selection in the Cox model with parametric relative risk, we consider the univariate shrinkage method (US) using the lasso penalty and the penalized partial likelihood method using the folded penalties (PPL). The penalization methods are not restricted to the finite-dimensional case. For the high-dimensional (p→∞,p≪n) or ultrahigh-dimensional case (n→∞,n≪p), both the sure independence screening (SIS) method and the extended Bayesian information criterion (EBIC) can be further incorporated into the penalization methods for variable selection. We also consider the penalization method for the Cox model with semiparametric relative risk, and the modified partial least squares method for the Cox model. The comparison of different methods is discussed and numerical examples are provided for the illustration. Finally, areas of further research are presented.

Incorporating biological networks into high-dimensional Bayesian survival analysis using an ICM/M algorithm

2021 ◽  
Author(s):  
Vitara Pungpapong
Keyword(s):  
Variable Selection ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Cox Model ◽  
Cox Proportional Hazards Model ◽  
Adaptive Lasso ◽  
Genomic Research ◽  
High Dimensional ◽  
Cox Models

The Cox proportional hazards model has been widely used in cancer genomic research that aims to identify genes from high-dimensional gene expression space associated with the survival time of patients. With the increase in expertly curated biological pathways, it is challenging to incorporate such complex networks in fitting a high-dimensional Cox model. This paper considers a Bayesian framework that employs the Ising prior to capturing relations among genes represented by graphs. A spike-and-slab prior is also assigned to each of the coefficients for the purpose of variable selection. The iterated conditional modes/medians (ICM/M) algorithm is proposed for the implementation for Cox models. The ICM/M estimates hyperparameters using conditional modes and obtains coefficients through conditional medians. This procedure produces some coefficients that are exactly zero, making the model more interpretable. Comparisons of the ICM/M and other regularized Cox models were carried out with both simulated and real data. Compared to lasso, adaptive lasso, elastic net, and DegreeCox, the ICM/M yielded more parsimonious models with consistent variable selection. The ICM/M model also provided a smaller number of false positives than the other methods and showed promising results in terms of predictive accuracy. In terms of computing times among the network-aware methods, the ICM/M algorithm is substantially faster than DegreeCox even when incorporating a large complex network. The implementation of the ICM/M algorithm for Cox regression model is provided in R package icmm, available on the Comprehensive R Archive Network (CRAN).

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