Survival Analysis with Correlated Endpoints : Joint Frailty-Copula Models. 1st ed. 2019

種類:

電子ブック

責任表示:

by Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau

出版情報:

Singapore : Springer Nature Singapore : Imprint: Springer, 2019

著者名:

• Emura, Takeshi.
• Matsui, Shigeyuki.
• Rondeau, Virginie.
• SpringerLink (Online service)

シリーズ名:

JSS Research Series in Statistics ;

ISBN:

9789811335167 [9811335168]    

注記:

Chapter 1: Setting the scene.-1.1 Endpoints -- 1.2 Benefits of investigating correlated endpoints -- 1.3 Copulas and frailty: a brief history -- References -- Chapter 2: Introduction to survival analysis .-2.1 Endpoint and censoring -- 2.2 Kaplan-Meier estimator and survival function -- 2.3 Hazard function -- 2.4 Log-rank test for two-sample comparison -- 2.5 Cox regression -- 2.6 Example of Cox regression -- 2.7 Likelihood inference under non-informative censoring -- 2.8 Theoretical notes -- 2.9 Exercises -- References -- Chapter 3: The joint frailty-copula model for correlated endpoints -- 3.1 Introduction -- 3.2 Semi-competing risks data -- 3.3 Joint frailty-copula model -- 3.4 Penalized likelihood with splines -- 3.5 Case study: ovarian cancer data -- 3.6 Technical note 1: Numerical maximization of the penalized likelihood -- 3.7 Technical note 2: LCV and choice of and -- 3.8 Exercises -- References -- Chapter 4: High-dimensional covariates in the joint frailty-copula model -- 4.1 Introduction -- 4.2 Tuk
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

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