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Titlebook: Survival Analysis; A Self-Learning Text David G. Kleinbaum,Mitchel Klein Textbook 2012Latest edition Springer Science+Business Media, LLC,

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书目名称Survival Analysis
副标题A Self-Learning Text
编辑David G. Kleinbaum,Mitchel Klein
视频video
概述Second edition of the text originally published in 1996.New material has been added and the original six chapters have been modified
丛书名称Statistics for Biology and Health
图书封面Titlebook: Survival Analysis; A Self-Learning Text David G. Kleinbaum,Mitchel Klein Textbook 2012Latest edition Springer Science+Business Media, LLC,
描述.This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as statisticians in academia who teach introductory and second-level courses on survival analysis. .The third edition continues to use the unique "lecture-book" format of the first two editions with one new chapter, additional sections and clarifications to several chapters, and a revised computer appendix. The Computer Appendix, with step-by-step instructions for using the computer packages STATA, SAS, and SPSS, is expanded this third edition to include the software package R..
出版日期Textbook 2012Latest edition
关键词Survival Analysis
版次3
doihttps://doi.org/10.1007/978-1-4419-6646-9
isbn_softcover978-1-4939-5018-8
isbn_ebook978-1-4419-6646-9Series ISSN 1431-8776 Series E-ISSN 2197-5671
issn_series 1431-8776
copyrightSpringer Science+Business Media, LLC, part of Springer Nature 2012
The information of publication is updating

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Introduction to Survival Analysis, type of problem addressed by survival analysis, the outcome variable considered, the need to take into account “censored data,” what a survival function and a hazard function represent, basic data layouts for a survival analysis, the goals of survival analysis, and some examples of survival analysis.
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Evaluating the Proportional Hazards Assumption,We begin with a brief review of the characteristics of the Cox proportional hazards (PH) model. We then give an overview of three methods for checking the PH assumption: graphical, goodness-of-fit (GOF), and time-dependent variable approaches.
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The Stratified Cox Procedure,We begin with an example of the use of the stratified Cox procedure for a single predictor that does not satisfy the PH assumption. We then describe the general approach for fitting a stratified Cox model, including the form of the (partial) likelihood function used to estimate model parameters.
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Extension of the Cox Proportional Hazards Model for Time-Dependent Variables,We begin by defining a time-dependent variable and providing some examples of such a variable. We also state the general formula for a Cox model that is extended to allow time dependent variables, followed by a discussion of the characteristics of this model, including a description of the hazard ratio.
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