عنوان

Survival analysis with correlated endpoints :

9789811335167

9789811335174

9811335168

9811335176

9789811335150

981133515X

Survival analysis with correlated endpoints :

[Book]

joint frailty-copula models /

Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau.

Singapore :

Springer,

2019.

1 online resource (xvii, 118 pages) :

illustrations (some color)

SpringerBriefs in statistics, JSS Research Series in Statistics,

2364-0057

Includes bibliographical references and index.

Intro; Preface; Use as a Textbook; Use as a Reference Book; Acknowledgements; Contents; Abbreviations; Notations; 1 Setting the Scene; 1.1 Endpoints and Censoring; 1.2 Motivations for Investigating Correlated Endpoints; 1.2.1 Understanding Disease Progression Mechanisms; 1.2.2 Dynamic Prediction of Death; 1.2.3 Validating Surrogate Endpoints; 1.3 Copulas and Bivariate Survival Models: A Brief History; References; 2 Introduction to Multivariate Survival Analysis; 2.1 Endpoints and Censoring; 2.2 Basic Terminologies; 2.3 Cox Regression; 2.3.1 R Survival Package; 2.4 Likelihood-Based Method

2.4.1 Spline and Penalized Likelihood2.5 Clustered Survival Data; 2.5.1 Shared Frailty Model; 2.5.2 Likelihood Function; 2.5.3 Penalized Likelihood and Spline; 2.6 Copulas for Bivariate Event Times; 2.6.1 Measures of Dependence; 2.6.2 Residual Dependence; 2.6.3 Likelihood Function; 2.7 Exercises; References; 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

6.4 Left Truncation6.5 Interactions; 6.5.1 (Gene × Gene) Interaction; 6.5.2 (Gene × Time) Interaction; 6.6 Parametric Failure Time Models; 6.7 Compound Covariate; References; Appendix A: Spline Basis Functions; Appendix B: R Codes for the Ovarian Cancer Data Analysis; B1. Using the CXCL12 Gene as a Covariate; B2. Using the Compound Covariates (CCs) and Residual Tumour as Covariates; Appendix C: Derivation of Prediction Formulas; Index

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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.

Springer Nature

com.springer.onix.9789811335167

Survival analysis with correlated endpoints.

9789811335150

Survival analysis (Biometry)

Survival analysis (Biometry)

MBNS

MED090000

PBT

PBT

Emura, Takeshi

Matsui, Shigeyuki

Rondeau, Virginie

20200823234316.0

pn

[Book]

Y