Survival Analysis
Download Survival Analysis PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Survival Analysis book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Survival Analysis
Author: David G. Kleinbaum
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-04-18
This text on smvival analysis methods contains the following chapters: 1 Introduction to Smvival Analysis 2 Kaplan-Meier Survival Curves and the Log-Rank Test 3 The Cox Proportional Hazards Model and Its Characteristics 4 Evaluating the Proportional Hazards Assumption 5 The Stratified Cox Procedure 6 Extension of the Cox Proportional Hazards Model for Time Dependent Variables Each chapter contains a presentation of its topic in '1ecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture-book" has a sequence of illustrations and formulae in the left column of each page and a script in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that high light the main points, formulae, or examples being presented. The reader may also purchase directly from the author audio cassette tapes of each chapter. The use of the audiotape with the illustrations and formu lae, ignoring the script, is intended to be similar to a lecture. Tapes may be obtained by writing or calling the author at the following address: Depart ment of Epidemiology, Rollins School of Public Health, Emory University, 1518 CliftoIi Rd. N. E. , Atlanta, GA 30322; phone (404) 727-9667. This text is intended for self-study.
Survival and Event History Analysis
Author: Odd Aalen
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-09-16
The aim of this book is to bridge the gap between standard textbook models and a range of models where the dynamic structure of the data manifests itself fully. The common denominator of such models is stochastic processes. The authors show how counting processes, martingales, and stochastic integrals fit very nicely with censored data. Beginning with standard analyses such as Kaplan-Meier plots and Cox regression, the presentation progresses to the additive hazard model and recurrent event data. Stochastic processes are also used as natural models for individual frailty; they allow sensible interpretations of a number of surprising artifacts seen in population data. The stochastic process framework is naturally connected to causality. The authors show how dynamic path analyses can incorporate many modern causality ideas in a framework that takes the time aspect seriously. To make the material accessible to the reader, a large number of practical examples, mainly from medicine, are developed in detail. Stochastic processes are introduced in an intuitive and non-technical manner. The book is aimed at investigators who use event history methods and want a better understanding of the statistical concepts. It is suitable as a textbook for graduate courses in statistics and biostatistics.
Bayesian Survival Analysis
Author: Joseph G. Ibrahim
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-03-09
Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. Several topics are addressed, including parametric models, semiparametric models based on prior processes, proportional and non-proportional hazards models, frailty models, cure rate models, model selection and comparison, joint models for longitudinal and survival data, models with time varying covariates, missing covariate data, design and monitoring of clinical trials, accelerated failure time models, models for mulitivariate survival data, and special types of hierarchial survival models. Also various censoring schemes are examined including right and interval censored data. Several additional topics are discussed, including noninformative and informative prior specificiations, computing posterior qualities of interest, Bayesian hypothesis testing, variable selection, model selection with nonnested models, model checking techniques using Bayesian diagnostic methods, and Markov chain Monte Carlo (MCMC) algorithms for sampling from the posteiror and predictive distributions. The book presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all essentially from the health sciences, including cancer, AIDS, and the environment. The book is intended as a graduate textbook or a reference book for a one semester course at the advanced masters or Ph.D. level. This book would be most suitable for second or third year graduate students in statistics or biostatistics. It would also serve as a useful reference book for applied or theoretical researchers as well as practitioners.