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The Cox Model and Its Applications

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概要 This book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox’s pioneering work in 19...72, the proportional hazards model has become the most important model in survival analysis. The success of the Cox model stimulated further studies in semiparametric and nonparametric theories, counting process models, study designs in epidemiology, and the development of many other regression models that could offer more flexible or more suitable approaches in data analysis. Flexible semiparametric regression models are increasingly being used to relate lifetime distributions to time-dependent explanatory variables. Throughout the book, various recent statistical models are developed in close connection with specific data from experimental studies in clinical trials or from observational studies.続きを見る
目次 Introduction: Several Classical Data Examples for Survival Analysis
Elements of Survival Analysis
The Cox Proportional Hazards Model
The AFT, GPH, LT, Frailty, and GLPH Models
Cross-effect Models of Survival Functions
The Simple Cross-effect Model
Goodness-of-Fit for the Cox Model
Remarks on Computations in Parametric and Semiparametric Estimation
Cox Model for Degradation and Failure Time Data
References
Index.
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本文を見る Full text available from Springer Mathematics and Statistics eBooks 2016 English/International

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登録日 2020.06.27
更新日 2020.06.28