Analysis of Survival Data with Dependent Censoring

Copula-Based Approaches

  • Takeshi Emura
  • Yi-Hau Chen

Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)

Also part of the JSS Research Series in Statistics book sub series (JSSRES)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Takeshi Emura, Yi-Hau Chen
    Pages 1-8
  3. Takeshi Emura, Yi-Hau Chen
    Pages 9-26
  4. Takeshi Emura, Yi-Hau Chen
    Pages 27-40
  5. Takeshi Emura, Yi-Hau Chen
    Pages 41-55
  6. Takeshi Emura, Yi-Hau Chen
    Pages 71-73
  7. Back Matter
    Pages 75-84

About this book


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.


Competing Risk Compound Covariate Cox Regression Kendall’s Tau Meta-Analysis Semi-Competing Risk Surrogate Endpoint

Authors and affiliations

  • Takeshi Emura
    • 1
  • Yi-Hau Chen
    • 2
  1. 1.Graduate Institute of StatisticsNational Central UniversityTaoyuanTaiwan
  2. 2.Institute of Statistical ScienceAcademia SinicaTaipeiTaiwan

Bibliographic information

  • DOI
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-981-10-7163-8
  • Online ISBN 978-981-10-7164-5
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site