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Analysis of Survival Data with Dependent Censoring: Copula-Based Approaches
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Catalogue Information
Field name
Details
Dewey Class
519.5
Title
Analysis of Survival Data with Dependent Censoring ([EBook]) : Copula-Based Approaches / by Takeshi Emura, Yi-Hau Chen.
Author
Emura, Takeshi
Added Personal Name
Chen, Yi-Hau
Other name(s)
SpringerLink (Online service)
Publication
Singapore : Springer Singapore , 2018.
Physical Details
XIII, 84 pages. 10 illus. : online resource.
Series
SpringerBriefs in Statistics
2191-544X
ISBN
9789811071645
Summary Note
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.:
Contents note
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.
System details note
Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users)
Internet Site
http://dx.doi.org/10.1007/978-981-10-7164-5
Links to Related Works
Subject References:
Biostatistics
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Statistical Theory and Methods
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Statistics
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Statistics for Life Sciences, Medicine, Health Sciences
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Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law
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Authors:
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Chen, Yi-Hau
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Emura, Takeshi
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SpringerLink (Online service)
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Series:
SpringerBriefs in Statistics
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Classification:
519.5
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519.5
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