Marginal variable screening for survival endpoints

Edelmann, Dominic; Hummel, Manuela; Hielscher, Thomas; Saadati, Maral; Benner, Axel

doi:10.1002/bimj.201800269

Cited by 8 publications

(7 citation statements)

References 48 publications

(85 reference statements)

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“…We list the common and effective survival feature screening methods, and provide readers with an overview summarized in Table 1 . Edelmann et al (2020) provided an R package “ MVS ” that can be downloaded from , which contains the first six screening methods discussed in Table 1 , and R codes for the last two screening methods discussed in Table 1 are available at , laying the foundation for meaningful comparison.…”

Section: Methodsmentioning

confidence: 99%

“…For the second simulated settings, we follow the simulation settings of Edelmann et al (2020) . The simulated settings are the same as the first simulated settings except for the relationships of true parameter vector, i.e.…”

Section: Methodsmentioning

confidence: 99%

“…Univariate feature screening for right-censored survival outcomes has been a challenging topic receiving much attention in the literature. Edelmann et al (2020) recently provided a comprehensive review and some useful suggestions for univariate feature independent screening methods, and pointed out these screening methods for survival traits can be roughly divided into the following categories: (semi-)parametric-based approaches, non-parametric ranking-based approaches, model-free approached based on conditional survival functions, and distance correlation-based approaches. All univariate independent feature screening methods are based on certain statistics with specific model assumptions, which are calculated for each variable without considering other variables.…”

Section: Introductionmentioning

confidence: 99%

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Feature screening for survival trait with application to TCGA high-dimensional genomic data

Wang¹,

Li²,

Hou³

2022

PeerJ

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Background In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA). Results Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas. Conclusions These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Feature screening for survival trait with application to TCGA high-dimensional genomic data

Wang¹,

Li²,

Hou³

2022

PeerJ

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“…The SIS and ISIS are routinely being applied in ultra-high dimensional applications and have also been extended to more complex models. 11–20 However, one major drawback of the SIS or ISIS is their non-robust nature against data contamination as indicated already in the discussion of the original paper itself. This issue can be crucial when applying the method for screening of important genes from large-scale Omics data, which are often prone to at least a few outliers.…”

Section: Introductionmentioning

confidence: 99%

A robust variable screening procedure for ultra-high dimensional data

Ghosh

Thoresen

2021

Stat Methods Med Res

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Variable selection in ultra-high dimensional regression problems has become an important issue. In such situations, penalized regression models may face computational problems and some pre-screening of the variables may be necessary. A number of procedures for such pre-screening has been developed; among them the Sure Independence Screening (SIS) enjoys some popularity. However, SIS is vulnerable to outliers in the data, and in particular in small samples this may lead to faulty inference. In this paper, we develop a new robust screening procedure. We build on the density power divergence (DPD) estimation approach and introduce DPD-SIS and its extension iterative DPD-SIS. We illustrate the behavior of the methods through extensive simulation studies and show that they are superior to both the original SIS and other robust methods when there are outliers in the data. Finally, we illustrate its use in a study on regulation of lipid metabolism.

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“…A simulation study in Section 4 compares a permutation test based on the estimator for distance covariance with tests based on the Cox model, thin‐plate regression splines (Wood, 2003), and a test adapted from the residual distance covariance in Edelmann et al. (2020). In Section 5, the distance covariance permutation test is applied on a gene expression dataset from breast cancer patients.…”

Section: Introductionmentioning

confidence: 99%

A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing

2021

Self Cite

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Distance covariance is a powerful new dependence measure that was recently introduced by Székely et al. and Székely and Rizzo. In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right-censored survival endpoint by establishing an estimator based on an inverse-probability-of-censoring weighted U-statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice.

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Marginal variable screening for survival endpoints

Cited by 8 publications

References 48 publications

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Feature screening for survival trait with application to TCGA high-dimensional genomic data

A robust variable screening procedure for ultra-high dimensional data

A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing

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