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Survival analysis with correlated en...
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Emura, Takeshi.
Survival analysis with correlated endpointsjoint frailty-copula models /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Survival analysis with correlated endpointsby Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau.
其他題名:
joint frailty-copula models /
作者:
Emura, Takeshi.
其他作者:
Matsui, Shigeyuki.
出版者:
Singapore :Springer Singapore :2019.
面頁冊數:
xvii, 118 p. :ill. (some col.), digital ;24 cm.
Contained By:
Springer eBooks
標題:
Survival analysis (Biometry)
電子資源:
https://doi.org/10.1007/978-981-13-3516-7
ISBN:
9789811335167$q(electronic bk.)
Survival analysis with correlated endpointsjoint frailty-copula models /
Emura, Takeshi.
Survival analysis with correlated endpoints
joint frailty-copula models /[electronic resource] :by Takeshi Emura, Shigeyuki Matsui, Virginie Rondeau. - Singapore :Springer Singapore :2019. - xvii, 118 p. :ill. (some col.), digital ;24 cm. - JSS research series in statistics,2364-0057. - JSS research series in statistics..
This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors' original R software package (freely available in CRAN) The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.
ISBN: 9789811335167$q(electronic bk.)
Standard No.: 10.1007/978-981-13-3516-7doiSubjects--Topical Terms:
182534
Survival analysis (Biometry)
LC Class. No.: QA276
Dewey Class. No.: 519.546
Survival analysis with correlated endpointsjoint frailty-copula models /
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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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