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Analysis of survival data with depen...

Emura, Takeshi.

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Analysis of survival data with dependent censoring = copula-based approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Analysis of survival data with dependent censoring/ by Takeshi Emura, Yi-Hau Chen.
Reminder of title:	copula-based approaches /
Author:	Emura, Takeshi.
other author:	Chen, Yi-Hau.
Published:	Singapore :Springer Singapore : : 2018.,
Description:	xiii, 84 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Setting the scene -- Chapter 2: Introduction to survival analysis -- Chapter 3: Copula models for dependent censoring -- Chapter 4: Gene selection under dependent censoring -- Chapter 5: The joint frailty-copula model for meta-analysis -- Chapter 6:High-dimensional covariates in the joint frailty-copula model -- Chapter 7:Dynamic prediction of time-to-death. Chapter 8: Future developments -- Appendix.
Contained By:	Springer eBooks
Subject:	Censored observations (Statistics) -
Online resource:	http://dx.doi.org/10.1007/978-981-10-7164-5
ISBN:	9789811071645

Analysis of survival data with dependent censoring = copula-based approaches /
Emura, Takeshi.

Analysis of survival data with dependent censoringcopula-based approaches /[electronic resource] :by Takeshi Emura, Yi-Hau Chen. - Singapore :Springer Singapore :2018. - xiii, 84 p. :ill., digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

Chapter 1: Setting the scene -- Chapter 2: Introduction to survival analysis -- Chapter 3: Copula models for dependent censoring -- Chapter 4: Gene selection under dependent censoring -- Chapter 5: The joint frailty-copula model for meta-analysis -- Chapter 6:High-dimensional covariates in the joint frailty-copula model -- Chapter 7:Dynamic prediction of time-to-death. Chapter 8: Future developments -- Appendix.

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients' survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers' understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.

ISBN: 9789811071645

Standard No.: 10.1007/978-981-10-7164-5doiSubjects--Topical Terms:

2072780
Censored observations (Statistics)

LC Class. No.: QA276.6 / .E487 2018

Dewey Class. No.: 519.546

Analysis of survival data with dependent censoring = copula-based approaches /
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300 $a xiii, 84 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-544X
505 0 $a Chapter 1: Setting the scene -- Chapter 2: Introduction to survival analysis -- Chapter 3: Copula models for dependent censoring -- Chapter 4: Gene selection under dependent censoring -- Chapter 5: The joint frailty-copula model for meta-analysis -- Chapter 6:High-dimensional covariates in the joint frailty-copula model -- Chapter 7:Dynamic prediction of time-to-death. Chapter 8: Future developments -- Appendix.
520 $a This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring. Primarily focusing on likelihood-based methods performed under copula models, it is the first book solely devoted to the problem of dependent censoring. The book demonstrates the advantages of the copula-based methods in the context of medical research, especially with regard to cancer patients' survival data. Needless to say, the statistical methods presented here can also be applied to many other branches of science, especially in reliability, where survival analysis plays an important role. The book can be used as a textbook for graduate coursework or a short course aimed at (bio-) statisticians. To deepen readers' understanding of copula-based approaches, the book provides an accessible introduction to basic survival analysis and explains the mathematical foundations of copula-based survival models.
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