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Survival Analysis with Correlated Endpoints

Joint Frailty-Copula Models

  • Takeshi Emura
  • Shigeyuki Matsui
  • Virginie Rondeau

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

Introduction

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.

Keywords

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

Authors and affiliations

  • Takeshi Emura
    • 1
  • Shigeyuki Matsui
    • 2
  • Virginie Rondeau
    • 3
  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

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-3516-7
  • Copyright Information The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-981-13-3515-0
  • Online ISBN 978-981-13-3516-7
  • Series Print ISSN 2191-544X
  • Series Online ISSN 2191-5458
  • Buy this book on publisher's site