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BL
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| Record Number
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889097
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| Main Entry
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Emura, Takeshi
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| Title & Author
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Analysis of survival data with dependent censoring : : copula-based approaches /\ Takeshi Emura, Yi-Hau Chen.
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| Publication Statement
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Singapore :: Springer,, 2018.
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| Series Statement
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SpringerBriefs in statistics. JSS research series in statistics,
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| Page. NO
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1 online resource (xiii, 84 pages) :: illustrations
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| ISBN
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9789811071638
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: 9789811071645
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: 9789811071652
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: 9811071632
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: 9811071640
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: 9811071659
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9789811071638
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Preface; About this Book; Use as a Textbook; References; Acknowledgements; Contents; Abbreviations; Notations; 1 Setting the Scene; Abstract; 1.1 Survival Analysis and Censoring; 1.2 Informative Dropout; 1.3 Benefits of Investigating Dependent Censoring; 1.3.1 Examining the Influence of Dependent Censoring; 1.3.2 Improving Prediction by Using Dependent Censoring; 1.4 Copulas and Survival Analysis: A Brief History; References; 2 Introduction to Survival Analysis; Abstract; 2.1 Survival Time; 2.2 Kaplan-Meier Estimator and Survival Function; 2.3 Hazard Function.
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2.4 Log-Rank Test for Two-Sample Comparison2.5 Cox Regression; 2.6 R Survival Package; 2.7 Likelihood-Based Inference; 2.8 Technical Notes; 2.9 Exercises; References; 3 Copula Models for Dependent Censoring; Abstract; 3.1 Introduction; 3.2 Bivariate Copula; 3.3 Dependence Measures; 3.4 Residual Dependence; 3.5 Biased Estimation of Cox Regression Due to Dependent Censoring; 3.6 Exercises; References; 4 Analysis of Survival Data Under an Assumed Copula; Abstract; 4.1 Introduction; 4.2 The Copula-Graphic (CG) Estimator; 4.3 Model and Likelihood; 4.4 Parametric Models; 4.4.1 The Burr Model.
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4.4.2 The Weibull Model4.4.3 The Pareto Model; 4.4.4 The Burr III Model; 4.4.5 The Piecewise Exponential Model; 4.5 Semi-parametric Models; 4.5.1 The Transformation Model; 4.5.2 The Spline Model; References; 5 Gene Selection and Survival Prediction Under Dependent Censoring; Abstract; 5.1 Introduction; 5.2 Univariate Selection; 5.3 Copula-Based Univariate Cox Regression; 5.4 Copula-Based Univariate Selection; 5.5 Choosing the Copula Parameter by the C-Index; 5.6 Lung Cancer Data Analysis; 5.6.1 Gene Selection and Prediction; 5.6.2 Assessing Prediction Performance; 5.7 Discussions; References.
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6 Future DevelopmentsAbstract; 6.1 Log-Rank Test Under Dependent Censoring; 6.2 Dependent Left-Truncation; References; Appendix A: Spline Basis Functions; Appendix B: R Codes for the Lung Cancer Data Analysis; Index.
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| Abstract
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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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| Subject
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Censored observations (Statistics)
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Copulas (Mathematical statistics)
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Censored observations (Statistics)
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Copulas (Mathematical statistics)
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| Subject
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MATHEMATICS-- Applied.
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| Subject
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MATHEMATICS-- Probability Statistics-- General.
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| Dewey Classification
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519.5/46
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| LC Classification
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QA276.6
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| Added Entry
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Chen, Yi-Hau
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