Feature screening in ultrahigh dimensional cox's model

Yang, Guangren; Yu, Yi; Li, Runze; Buu, Anne

doi:10.5705/ss.2014.171

Cited by 24 publications

(46 citation statements)

References 20 publications

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“…With redundant gene elimination by comparing the Cox filter method with RGE and the original Cox filter method, the resulting signatures have better predictive performance, smaller model sizes and more subtype-specific genes. Furthermore, the present study demonstrated that the use of a pre-filtering process prior to downstream analysis is very beneficial, which is consistent with previous findings by the authors ( 9 ) and the work by others ( 38 , 39 ). Therefore, it is highly recommended to carry out the pre-filtering process, particularly when a very complicated and time-consuming statistical method was selected for downstream analysis.…”

Section: Discussionsupporting

confidence: 93%

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Tian

2018

Oncol Lett

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Lung cancer (LC) is a leading cause of cancer-associated mortalities worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) account for ~70% of all cases of LC. Since AC and SCC are two distinct diseases, their corresponding prognostic genes associated with patient survival time are expected to be different. To date, only a few studies have distinguished patients with good prognosis from those with poor prognosis for each specific subtype. In the present study, the Cox filter model, a feature selection algorithm that identifies subtype-specific prognostic genes to incorporate pathway information and eliminate redundant genes, was adopted. By applying the proposed model to data on non-small cell lung cancer (NSCLC), it was demonstrated that both redundant gene elimination and search space restriction can improve the predictive capacity and the model stability of resulting prognostic gene signatures. To conclude, a pre-filtering procedure that incorporates pathway information for screening likely irrelevant genes prior to complex downstream analysis is recommended. Furthermore, a feature selection algorithm that considers redundant gene elimination may be preferable to one without such a consideration.

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Section: Discussionsupporting

confidence: 93%

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Tian

2018

Oncol Lett

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“…With the advent of the biomedical big data era, variable screening for ultrahigh dimensional survival data has been rapidly evolving; see some recent works in [26], [22], [24], and [23].…”

Section: §4 Discussionmentioning

confidence: 99%

Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

Hong

2017

Appl. Math. J. Chin. Univ.

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Many modern biomedical studies have yielded survival data with high-throughput predictors. The goals of scientific research often lie in identifying predictive biomarkers, understanding biological mechanisms and making accurate and precise predictions. Variable screening is a crucial first step in achieving these goals. This work conducts a selective review of feature screening procedures for survival data with ultrahigh dimensional covariates. We present the main methodologies, along with the key conditions that ensure sure screening properties. The practical utility of these methods is examined via extensive simulations. We conclude the review with some future opportunities in this field. MR Subject Classification 97K80Keywords survival analysis; ultrahigh dimensional predictors; variable screening; sure screening property §1 Introduction Modern biomedical studies have generated abundant survival data with high dimensional biomarkers for various scientific purposes. For instance, identifying genomic profiles that are associated with cancer patients' survival may help with understanding disease progression processes and designing more effective gene therapies. With the advent of new biotechnologies, the emergence of high-throughput data, such as gene expressions, SNPs, methylation and next-generation RNA sequencing, has pushed the dimensionality of data to a larger scale. In these cases, the dimensionality of covariates may grow exponentially with the sample size and such data has been commonly referred to as ultrahigh dimensional data ([5]).When the number of covariates (p) is less than the sample size (n), the parametric regression, such as Weibull models, and the semiparametric regression, such as the Cox proportional hazards model and the Accelerated Failure Time (AFT) model, have been routinely used for modeling censored outcome data in many practical settings. When p > n, penalized ([20], [4], [25], [29]) and the oracle properties and statistical error bounds of estimation have been established ([13], [15]). However, when p ≫ n, computational issues inherent in these methods makes them nonapplicable to ultrahigh-dimensional statistical learning problems because of serious challenges in "computational expediency, statistical accuracy, and algorithmic stability" ([6]). A recent work by [2] did establish the oracle properties of the regularized partial likelihood estimates under an ultrahigh dimensional setting. The results, however, required the optimizers to the penalized partial likelihood function to be unique and global, which is, in general, difficult to verify, especially when the dimension of covariates is exceedingly high. HHS Public AccessA seminal paper by [5] has demonstrated a simple but useful way to deal with ultrahigh dimensional regression. First, a variable screening procedure is used as a fast and crude tool for reducing the dimensionality to a moderate size (usually below the sample size). In the second step, a more sophisticated technique, such as penalized likelihood methods, c...

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“…Remark Although we allow p n to diverge, the asymptotic properties of the BAR estimator in the Section 2.1 are derived for p n < n . In an ultrahigh‐dimensional setting where the number of covariates far exceeds the number of observations ( p n ≫ n ), one may couple a sure screening method with the BAR estimator to obtain a two‐step estimator with desirable selection and estimation properties.…”

Section: Methodsmentioning

confidence: 99%

“…The orders of q n , p n , and n and their relationships depend on the employed screening procedure. For example, coupling the BAR estimator with the sure joint screening procedure has been explored in the work of Kawaguchi…”

Section: Methodsmentioning

confidence: 99%

A surrogateℓ₀sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data

Kawaguchi

Suchard

Liu

et al. 2019

Statistics in Medicine

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Sparse high‐dimensional massive sample size (sHDMSS) time‐to‐event data present multiple challenges to quantitative researchers as most current sparse survival regression methods and software will grind to a halt and become practically inoperable. This paper develops a scalable ℓ0‐based sparse Cox regression tool for right‐censored time‐to‐event data that easily takes advantage of existing high performance implementation of ℓ2‐penalized regression method for sHDMSS time‐to‐event data. Specifically, we extend the ℓ0‐based broken adaptive ridge (BAR) methodology to the Cox model, which involves repeatedly performing reweighted ℓ2‐penalized regression. We rigorously show that the resulting estimator for the Cox model is selection consistent, oracle for parameter estimation, and has a grouping property for highly correlated covariates. Furthermore, we implement our BAR method in an R package for sHDMSS time‐to‐event data by leveraging existing efficient algorithms for massive ℓ2‐penalized Cox regression. We evaluate the BAR Cox regression method by extensive simulations and illustrate its application on an sHDMSS time‐to‐event data from the National Trauma Data Bank with hundreds of thousands of observations and tens of thousands sparsely represented covariates.

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Feature screening in ultrahigh dimensional cox's model

Cited by 24 publications

References 20 publications

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

A surrogateℓ₀sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data

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Feature screening in ultrahigh dimensional cox's model

Cited by 24 publications

References 20 publications

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Identification of subtype‑specific prognostic signatures using Cox models with redundant gene elimination

Feature selection of ultrahigh-dimensional covariates with survival outcomes: a selective review

A surrogateℓ0sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data

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A surrogateℓ₀sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data