Gene selection for survival data under dependent censoring: A copula-based approach

Emura, Takeshi; Chen, Yi‐Hau

doi:10.1177/0962280214533378

Cited by 67 publications

(71 citation statements)

References 37 publications

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“…2, which can serve as a post hoc guide to the paper. Copula approaches have been used to great effect in neuroscience (Onken et al 2009), bioinformatics (Kim et al 2008), medical research (Emura & Chen 2016), direct study of environmental variables (Serinaldi 2008; Li et al 2013; Goswami et al 2018; She & Xia 2018), and finance (Li 2000), and have also been used very effectively, but rarely so far, in ecology (Valpine et al 2014; Anderson et al 2018; Popovic et al 2018). We argue that benefits of wider usage of complete copula descriptions of dependence in ecology will be substantial.…”

Section: Introductionmentioning

confidence: 99%

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Copulas and their potential for ecology

Ghosh

Sheppard

Holder

et al. 2019

Preprint

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AbstractAll branches of ecology study relationships among and between environmental and biological variables. However, standard approaches to studying such relationships, based on correlation and regression, provide only a small slice of the complex information contained in the relationships. Other statistical approaches exist that provide a complete description of relationships between variables, based on the concept of the copula; they are applied in finance, neuroscience and other fields, but rarely in ecology. We here explore the concepts that underpin copulas and examine the potential for those concepts to improve our understanding of ecology. We find that informative copula structure in dependencies between variables is common across all the environmental, species-trait, phenological, population, community, and ecosystem functioning datasets we considered. Many datasets exhibited asymmetric tail associations, whereby two variables were more strongly related in their left compared to right tails, or vice versa. We describe mechanisms by which observed copula structure and asymmetric tail associations can arise in ecological data, including a Moran-like effect whereby dependence structures between environmental variables are inherited by ecological variables; and asymmetric or nonlinear influences of environments on ecological variables, such as under Liebig’s law of the minimum. We also describe consequences of copula structure for ecological phenomena, including impacts on extinction risk, Taylor’s law, and the stability through time of ecosystem services. By documenting the importance of a complete description of dependence between variables, advancing conceptual frameworks, and demonstrating a powerful approach, we aim to encourage widespread use of copulas in ecology, which we believe can benefit the discipline.

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Section: Introductionmentioning

confidence: 99%

“…Section 9 is the Discussion. Copula approaches have been used to great effect in neuroscience (Onken et al 2009), bioinformatics (Kim et al 2008), medical research (Emura & Chen 2016), direct study of environmental variables (Serinaldi 2008; Li et al . 2013; Goswami et al .…”

Section: Introductionmentioning

confidence: 99%

Copulas and their potential for ecology

Ghosh

Sheppard

Holder

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…A large number of competing approaches appears possible since there is a large body of literature on goodness-of-fit procedures and a large body on predictive measures. Combinations of these could provide tools analogous to those described here and this has already been considered by some authors (Emura and Chen, 2014;Austin, Pencina, and Steyerberg, 2015). However, in order to make analogous claims to ours concerning predictive performance for some particular combination, we would require equivalent theorems to those presented in Sections 2 and 3.…”

Section: Discussionmentioning

confidence: 78%

Survival Model Construction Guided by Fit and Predictive Strength

Chauvel

O’Quigley

2016

Biometrics

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We describe a unified framework within which we can build survival models. The motivation for this work comes from a study on the prediction of relapse among breast cancer patients treated at the Curie Institute in Paris, France. Our focus is on how to best code, or characterize, the effects of the variables, either alone or in combination with others. We consider simple graphical techniques that not only provide an immediate indication as to the goodness of fit but, in cases of departure from model assumptions, point in the direction of a more involved alternative model. These techniques help support our intuition. This intuition is backed up by formal theorems that underlie the process of building richer models from simpler ones. Goodness-of-fit techniques are used alongside measures of predictive strength and, again, formal theorems show that these measures can be used to help identify models closest to the unknown non-proportional hazards mechanism that we can suppose generates the observations. We consider many examples and show how these tools can be of help in guiding the practical problem of efficient model construction for survival data.

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“…In the absence of covariates, these papers proposed semiparametric inference methods assuming a parametric copula with unspecified marginal distributions for the truncation time and lifetime. The copulabased models could be extended to include covariates along the line of Braekers and Veraverbeke (2005), Chen (2010), and Emura and Chen (2014) as studied under the competing risks setting. However, one difficulty comes from the joint estimation of the two marginal distribution functions, which are infinite dimensional.…”

Section: Discussionmentioning

confidence: 99%

Semiparametric inference for an accelerated failure time model with dependent truncation

Emura

Wang

2015

Ann Inst Stat Math

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Truncated data are commonly seen in studies of biomedicine, epidemiology, astronomy and econometrics. Existing regression methods for analyzing left-truncated and right-censored data have been developed under the assumption that the lifetime variable of interest is independent of both truncation and censoring variables. In this article, we propose a semiparametric accelerated failure time model that incorporates both covariates and the truncation variable as regressors. The proposed model utilizes the truncation information in statistical modeling and hence allows for dependent truncation. For estimation, we develop a set of estimating equations constructed from the log-rank and quasi-independence test statistics. We show that the resulting estimators are consistent and asymptotically normal. We also propose an explicit formula for variance estimation based on a kernel method. Finite-sample performances of the estimators are studied by simulations. The proposed methodology is applied to analyze a real data for illustration.

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Gene selection for survival data under dependent censoring: A copula-based approach

Cited by 67 publications

References 37 publications

Copulas and their potential for ecology

Copulas and their potential for ecology

Survival Model Construction Guided by Fit and Predictive Strength

Semiparametric inference for an accelerated failure time model with dependent truncation

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