The Cox Model and Its Applications

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics, Statistics
Cover of the book The Cox Model and Its Applications by Mikhail Nikulin, Hong-Dar Isaac Wu, Springer Berlin Heidelberg
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Author: Mikhail Nikulin, Hong-Dar Isaac Wu ISBN: 9783662493328
Publisher: Springer Berlin Heidelberg Publication: April 11, 2016
Imprint: Springer Language: English
Author: Mikhail Nikulin, Hong-Dar Isaac Wu
ISBN: 9783662493328
Publisher: Springer Berlin Heidelberg
Publication: April 11, 2016
Imprint: Springer
Language: English

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 1972, 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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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 1972, 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.

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