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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.
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