© 2019

Survival Analysis with Correlated Endpoints

Joint Frailty-Copula Models

  • The first-ever book tailored to the problem of correlated endpoints in survival analysis

  • Offers a clearly structured textbook on survival analysis, suitable for graduate students, (bio)statisticians, mathematicians, and medical researchers alike

  • Helps readers apply the statistical methods of this book to real data, by means of the R package


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-xvii
  2. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 1-8
  3. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 9-37
  4. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 39-58
  5. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 59-75
  6. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 77-93
  7. Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau
    Pages 95-103
  8. Back Matter
    Pages 105-118

About this book


This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies.

In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model.

To help readers apply the statistical methods to real-world data, the book provides case studies using the authors’ original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.


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

Authors and affiliations

  1. 1.Graduate Institute of StatisticsNational Central UniversityTaoyuan CityTaiwan
  2. 2.Department of Biostatistics, Graduate School of MedicineNagoya UniversityNagoyaJapan
  3. 3.INSERM CR1219 (Biostatistic)University of BordeauxBordeaux CedexFrance

About the authors

Takeshi Emura, Chang Gung University

Shigeyuki Matsui, Department of Biostatistics, Nagoya University Graduate School of Medicine 

Virginie Rondeau, INSERM U 1219

Bibliographic information

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“This book can be used as a textbook for a course aimed at postgraduate students in biostatistics and medicine.” (Denis Sidorov, zbMATH 1429.62003, 2020)