Book Description
Biomedical and social science researchers who want to analyze survival data with the SAS System will find just what they need with this easy-to-read and comprehensive guide. Written for the person with a modest statistical background and minimal knowledge of SAS software, this book teaches many aspects of data input and manipulation. Numerous examples of SAS code and output make this an eminently practical book ensuring that even the uninitiated becomes a sophisticated user of survival analysis. The main topics presented include censoring, survival curves, Kaplan-Meier estimation, accelerated failure time models, Cox regression models, and discrete-time analysis. Also included are topics not usually covered such as time-dependent covariates, competing risks, and repeated events.
Supports releases 6.09 and higher of SAS software.
Customer Reviews:
Nice reference for survival analysis.......2007-01-11
So far, this book has been a useful reference for survival analysis. It is clearly written and the xplanatins are understandable and helpful. It would be nice to have a newer edition that addresses changes in later versions of SAS.
Learn By Doing.......2005-06-14
If you have data that fit the general category "time to event," and are not suitably analyzed by repeated measures ANOVA, you are probably looking at doing a survival analysis (also known by several other names). If you are working largely on your own, and you learn best by doing, you cannot--as far as I know--do better than Allison's book. Of course it all but locks you into using SAS for analyses, but his explanations of proportional hazards and other models are the best I've found among a dozen textbooks and stats package manuals (some of which made sense only after reading Allison). What makes this book so good is that it will have you running your analyses in just hours. The examples are superb take-off points. I was not a SAS user before reading the book and therefore took a little extra time to figure out dataset manipulations and such in SAS, but that was minor effort compared to the rewards of having Allison's clearly written book as a guide. The price of this book represents only a fraction of its value.
Extraordinarily Clear and Useful.......2000-02-06
I've used a number of this author's books and they all share in common lucidity, utility, and rigor. This book makes it easy to grasp complex ideas, provides comprehensible examples, gives sample SAS code so that implementing the methods is as straightforward as possible. Plus, it is clear that the author is a subtle and first-rate methodologist, who innovates in this area as well as teaches it.
Best how-to book on survival analysis using SAS. Very useful.......1999-06-22
This book is well-written, well-organized, and very practical. I found it invaluable in conducting my research. My only recommendation for the author for his next edition is to include a chapter on dealing with correlated event times, like time-to-promotion and time-to-quiting in his policemen example (pg 249).
Book Description
* Contains additional discussion and examples on left truncation as well as material on more general censoring and truncation patterns.
* Introduces the martingale and counting process formulation swil lbe in a new chapter.
* Develops multivariate failure time data in a separate chapter and extends the material on Markov and semi Markov formulations.
* Presents new examples and applications of data analysis.
Customer Reviews:
A welcome and well-written update to a classic in the field. .......2005-08-25
The prior edition of this book has long been used for introductory courses in survival analysis for statistics students, and its treatment of the proportional hazards model and partial likelihood is classic. Contrary to the claims of another reviewer here, notation for the survival function is far from standardized in the field. In fact, both this book and another standard text ("Analysis of Survival Data" by D.R. Cox and D. Oakes) represent this quantity with an "F". An excellent and authoratative introduction for students with some knowledge of theoretical statistics.
Book Description
Third Edition brings the text up to date with new material and updated references.
- New content includes an introduction to left and interval censored data; the log-logistic distribution; estimation procedures for left and interval censored data; parametric methods iwth covariates; Cox's proportional hazards model (including stratification and time-dependent covariates); and multiple responses to the logistic regression model.
- Coverage of graphical methods has been deleted.
- Large data sets are provided on an FTP site for readers' convenience.
- Bibliographic remarks conclude each chapter.
Customer Reviews:
It's the best resource I found for survival data analysis!.......1997-10-21
As a graduate student in epidemiology who is incessantly looking for better ways to learn abstract concepts in statistics, I highly recommend this book by Elisa Lee. It's one of the few books that I found which explains advanced level statistics, such as parametric and non-parametric analysis, in a way that non-statisticans like myself can understand. It's also a handy reference to have at your side while reading the methods section of journal articles.
Book Description
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"The book is a valuable completion of the literature in this field. It is written in an ambitious mathematical style and can be recommended to statisticians as well as biostatisticians."
-Biometrische Zeitschrift
"Not many books manage to combine convincingly topics from probability theory over mathematical statistics to applied statistics. This is one of them. The book has other strong points to recommend it: it is written with meticulous care, in a lucid style, general results being illustrated by examples from statistical theory and practice, and a bunch of exercises serve to further elucidate and elaborate on the text."
-Mathematical Reviews
"This book gives a thorough introduction to martingale and counting process methods in survival analysis thereby filling a gap in the literature."
-Zentralblatt für Mathematik und ihre Grenzgebiete/Mathematics Abstracts
"The authors have performed a valuable service to researchers in providing this material in [a] self-contained and accessible form. . . This text [is] essential reading for the probabilist or mathematical statistician working in the area of survival analysis."
-Short Book Reviews, International Statistical Institute
Counting Processes and Survival Analysis explores the martingale approach to the statistical analysis of counting processes, with an emphasis on the application of those methods to censored failure time data. This approach has proven remarkably successful in yielding results about statistical methods for many problems arising in censored data. A thorough treatment of the calculus of martingales as well as the most important applications of these methods to censored data is offered. Additionally, the book examines classical problems in asymptotic distribution theory for counting process methods and newer methods for graphical analysis and diagnostics of censored data. Exercises are included to provide practice in applying martingale methods and insight into the calculus itself.
Customer Reviews:
Well-written and Informative.......2005-06-06
This is one of the best treatments I have read on the martingale theory of the analysis of survival data. This material really gets you under-the-hood of proportional hazards modeling and details when the theory is applicable and where things can break down in the models. This is positively a "must-own" for advanced researchers working with survival data and a "good-to-have" desk reference for anyone dealing with survival data.
Chapter 0 provides a meaningful introduction, and the authors use real-world survival data sets to set the stage for the basic concepts. They like the PBC liver study data set a lot and use it frequently through the text. There is some great material in this short chapter, including a formal definition of the hazard function, a nice overview of the Kaplan-Meier estimator, and an introduction of the Cox model with a very nice, intuitive treatment of the derivation of Cox's partial likelihood function. The authors also set the stage for the martingale theoretic treatment and give three motivating (and compelling) reasons for the theory.
Chapter 1 covers the basics from stochastic analysis that are required for the remainder of the book. Basic definitions and concepts like filtration, conditional expectation, the definition of a martingale and the Doob-Meyer decomposition are covered. No prior knowledge of stochastic analysis is assumed. However, a good understanding of measure theory is very helpful (something along the lines of the first four chapters for Rudin's Real and Complex Analysis, 3rd edition). The chapter wraps up with the martingale transformation theorem.
The main aim of Chapter 2 is to establish quadratic variation properties for continuous compensators of counting processes. This material is heavily used in the asymptotic Brownian motion material in Chapter 5 (where a large part of the story rests on the limiting behavior of quadratic variation). To get there, a number of localization results are established. The Optional Sampling Theorem is stated and used (the proof is referenced out to the literature). The main workhorse, the Optional Stopping Theorem is established as a nice application of optional sampling.
Chapter 3 is a wonderful, rigorous treatment of the survival estimators and test statistics that we know and love and always wondered why these are vaguely true. The main result is the consistency of the Kaplan-Meier estimator, which foreshadows the consistency results for the Cox regression estimator established in Chapter 8.
The proportional hazards model and multiplicative intensity models are the main focus of Chapter 4. The modeling framework is introduced, basic concepts such as uninformative censoring are introduced and the method of partial likelihoods is explored in depth. The chapter just has great little pearls sprinkled throughout, including martingale properties for Breslow's estimator for baseline hazard and a number of modeling building diagnostic techniques. There is also a very nice set of graphs on the martingale residual technique of assessing functional form of continuous covariates.
Chapter 5 is the core of the book and develops the asymptotic limit results, including the martingale central limit theorem for counting processes. The chapter is nearly self-contained, with the occasional reference to one of the classical probability texts like Chung or Billingsley. Proofs that could prove a distraction to the main thread are placed in the appendix.
Chapters 6, 7 and 8 provide very nice applications of the martingale central limit theorem. These include: building confidence bands, establishing large sample properties of test statistics and putting Cox's technique of partial likelihoods on solid footing by establishing by establishing consistency and asymptotic normality.
As a wish list item for the next edition, it would be nice to see a chapter or two covering Markov Processes and Competing Risks.
Average customer rating:
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Survival and Event History Analysis (Wiley Reference Series in Biostatistics)
Manufacturer: Wiley
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ASIN: 0470058064 |
Book Description
A unique and invaluable reference resource for those working in survival analysis.
Survival analysis is concerned with studying the time between entry to a study and a subsequent event. Originally the analysis was concerned with time from treatment until death, hence the name, but survival analysis is applicable to many areas in addition to mortality, such as recidivism or the efficacy of drugs. The techniques can also be used in engineering and quality control (e.g. how long is a particular component likely to last?).
Arranged in an A-Z format, Survival and Event History Analysis edited by Niels Keiding and Per Kragh Andersen contains 96 articles written by over 60 distinguished authors. The articles are taken from the Encyclopedia of Biostatistics, 2nd Edition.
This book gives the reader a thorough grounding in the subject area and the extensive references at the end of each article provide a comprehensive source of information for further information in more depth.
Book Description
Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.
Customer Reviews:
Not helpful!.......2007-03-09
I ordered this book for a class i am taking as part of a graduate degree. I read only the first and second chapter. The first one was o.k, you can get most of it, as for chapter tow, the notation and command for R software are not self explanatory.
If this is the first time you are introduced to the Survival analysis subject, this is not a helpful book.
Average customer rating:
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Survival Models and Data Analysis (Wiley Series in Probability and Statistics)
Regina C. Elandt-Johnson , and
Norman L. Johnson
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover
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ASIN: 0471031747 |
Book Description
Other volumes in the Wiley Series in Probability and Mathematical Statistics: Ralph A. Bradley, J. Stuart Hunter, David G. Kendall, and Geoffrey S. Watson— Advisory Editors The Statistical Analysis of Failure Time Data John D. Kalbfleisch & Ross L. Prentice This volume collects and unifies statistical models proposed for the analysis of failure time data in the biomedical, industrial, and engineering sciences. The focus is on regression problems with survival data, specifically estimation of regression coefficients and distributional shape in the presence of censoring. Contains specific biographical notes, historical summaries, theoretical and applied problems, numerous worked examples, and computer programs. 1980 Biostatistics Casebook Edited by Rupert G. Miller, Jr., Bradley Efron, Byron Wm. Brown, Jr., and Lincoln E. Moses This book deals with the statistical aspects of actual biomedical research problems. It provides enough of the scientific background of each problem to guide the statistical approach. Using the case study method, the book discusses many new and specially developed concepts and techniques, often applying a variety of techniques to the same detailed data set. 1980 Survival Distributions: Reliability Applications in the Biomedical Sciences Alan J. Gross & Virginia A. Clark "This book is clearly arranged… [and] can be recommended to students and to those who want to become acquainted with the techniques for analysing life test data from the practical standpoint."—Technometrics Describes nonparametric and parametric techniques used to achieve more reliable survival distributions in biomedical applications. Introduces commonly used survival distributions and covers applications of clinical life tables. Includes mathematical and graphical techniques for accurately selecting appropriate survival distributions to fit survival data, models for analyzing growth in reliability for clinical trials and industrial applications, a complete methodology for comparing two treatment groups when length of survival is the comparison criterion, and new help for choosing, in advance of clinical trial, the number of patients required for an adequate sample. 1975
Book Description
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 multivariate survival data, and special types of hierarchical 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. Joseph G. Ibrahim is Associate Professor of Biostatistics at the Harvard School of Public Health and Dana-Farber Cancer Institute; Ming-Hui Chen is Associate Professor of Mathematical Science at Worcester Polytechnic Institute; Debajyoti Sinha is Associate Professor of Biostatistics at the Medical University of South Carolina.
Customer Reviews:
Nice survey of Bayesian model selection.......2005-05-15
The authors have prepared a very nice survey-style treatment of Bayesian model building and specification with applications to the Cox theory of hazard models. The text is quite accessible; however, there isn't a lot of theory here. You'll need a little background material before jumping into this book. Reasonable prerequisites are Hosmer & Lemeshow's "Applied Survival Analysis" and "Bayesian Data Analysis" by Gelman, et al.
In Chapter 1, the authors provide a quick review of survival analysis before setting up the Bayesian modeling paradigm. For the Bayesians, the problem of inference of an unknown parameter is broken down into two components (thanks to Bayes' Theorem). The first component represents the contribution from the observed data set (the likelihood function). The second, and often troublesome, component comes from an assumption about the distribution of the unknown parameter, called the prior distribution. The two components combine in a natural way to give the inference. This it the so-called posterior distribution and is the goal of a Bayesian analyst.
We can therefore think of the Bayesian modeling problem as the need to acquire observed data, make a model selection and choose a prior distribution. Given these three elements, it is a straightforward application of Markov Chain Monte Carlo techniques (e.g. Gibbs Sampler) to fit the model and obtain parameter estimates.
Chapter 2 begins the survey of available Bayesian models for survival data by considering parametric survival models. This chapter gives a nice illustration of Bayesian model fitting techniques for some basic survival model techniques. Readers of the Cox theory may find themselves thinking that the parametric models presented have not one parametric item (the form of the hazard function) but two, the prior distribution of the beta coefficients.
The focus of Chapter 3 is a survey of semi-parametric models. These models are semi-parametric in the sense that the over-all form of the model is selected (usually some variation of the Cox Model), but the baseline hazard is unspecified by the standard theory. The Bayesian theory approaches the problem of the unspecified baseline by assuming its prior distribution changes with time as some identified stochastic process. The authors focus on the Gamma distribution and the Gamma process (a type of Levy process) for the first part of the chapter. Beta process models and their generalization, Dirichlet process models are presented next, but notably the treatment here isn't flexible enough to allow the model to include subject-specific covariates.
It is often the case in fitting survival data to a Cox model that one finds the proportional hazard assumptions fail to hold. Chapter 4 discusses this heterogeneity, called subject specific frailty, and surveys the Bayesian approach to fitting frailty models. Models using the Gamma distribution to encode frailty are examined from the finite variance perspective. The failure of these models to recapture the proportional hazards assumptions is discussed and the infinite variance positive stable distribution is discussed as technique to recapture proportionality into the model. The chapter ends with section discussing frailty from the point-of-view of competing risks models (multivariate survival models).
Chapter 5 is a short, but nicely prepared chapter on cure rate models. These are a family of models which incorporate recovery rates in the classical fatality time models. The authors discuss parametric and semi-parametric models from the cure rate perspective.
A collection of Bayesian model comparison techniques are offered in Chapter 6, including Bayes Factors, calculating posterior model probabilities, the Bayesian Information Criterion, the Conditional Predictive Ordinate along with the L measure tests. These tests can be used as part of a covariate selection scheme for a particular model or in a hypothesis test comparing two different models.
Chapter 7 discusses handling time-varying covariates and motivates this with longitudinal data modeling. Joint models are discussed and the EM algorithm is mentioned as the estimation technique of choice for fitting this models.
In the last three chapters, the authors turn to the problem of actually using Bayesian models in a real-world environment. Practical considerations such as missing data, model diagnostics, goodness-of-fit and questions of sample size are addressed. The Polya Tree process (a generalization of the Dirichlet process) is discussed as a way to address the shortcomings that the Dirichlet process prior models have with regards to subject-specific covariates.
The book contains HTTP links to download the data sets analyzed in this text. The authors also provide links to freeware code sources for the BUGS implementation of the Gibbs sampler the authors used throughout to fit their models.
Bayesian survival analysis.......2002-01-17
This is a very well written book and the first of its kind on Bayesian survival analysis. The authors have a very keen sense of the important issues and models in this area, and they do a wonderful job of presenting the various topics. The book discusses state-of-the-art methods for fitting Bayesian survival models. The content on the power prior and its uses in survival analysis was very exciting. The motivating examples in Chapter 1 were novel and very appealing. The authors have a great deal of experience in this area and in the applications they present. I definitely recommend buying this book. It serves as an exceptional reference or textbook.
A Great Book.......2002-01-14
This is truly a marvelous book on Bayesian survival analysis.
The authors, who are true experts in the field, have written
a gem that covers modern Bayesian methods in survival analysis.
They have a nice blend between modeling, theory, and applications
that truly makes this book the first of its kind. It has some
very nicely written chapters on semiparametric models based on
prior processes and frailty models. The book is very extensive
in its coverage and has a very long bibliography. This book is
going to be a best seller for a long time.
a great book.......2002-01-11
This is a fabulous book covering an extensive number of topics
in Bayesian Survival Analysis. This book will be hot for
biostatisticians as well as statisticians interested in
survival analysis. A great buy.
Bayesian Survival Analysis Review.......2002-01-11
This is a superb book! I really liked the chapters on
cure rate models and joint models for longitudinal and
survival data. Very cutting edge stuff.
Book Description
Praise for the First Edition
"An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ."
-Choice
"This is an important book, which will appeal to statisticians working on survival analysis problems."
-Biometrics
"A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook."
-Statistics in Medicine
The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data.
Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts.
New and expanded coverage in this edition includes:
* Observation schemes for lifetime data
* Multiple failure modes
* Counting process-martingale tools
* Both special lifetime data and general optimization software
* Mixture models
* Treatment of interval-censored and truncated data
* Multivariate lifetimes and event history models
* Resampling and simulation methodology
Customer Reviews:
Excellent for Pre-Multivariate Survival Analysis.......2006-10-05
This is one of the best books about survival data analysis, or lifetime analysis. This book covers univariate survival data analysis, providing necessary mathematical details. But it does not deal with multivariate cases. If you are climbing from the univariate toward multivariate, and taking a rest, this is perfect. This books is kind. However, watch two warnings. If you need accompanying software manuals, this book doesn't provide S-Plus, SAS, Stata or other advanced software code. Second, if you need competing risk or multivariate model, try others, including Hougaard or Cox. Elisa T. Lee's book presents less detail, but is still excellent, or may be better, depending reader's needs.
one of my favorite books on survival analysis.......2001-04-21
When I started my biostatistical career in 1995 at a medical device company this was the book I relied on for valuable reference information on life tables and survival curves. This book is particularly good at dealing with nonparametric methods and covering the distinctions between the various types of censoring. There are now also a number of other good books with more recent developments. Nelson's book was a competitor. Under the subject of reliability the same important paramatric models are covered in such books as the one by Mann, Schafer and Singpurwalla, the recent text by Meeker and Escobar and the book by Blischke and Murthy. Hougaard cover multivariate models.
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- The Shape of Space (Pure and Applied Mathematics)
- The Singularity Is Near: When Humans Transcend Biology
- The Transforms and Applications Handbook, Second Edition (The Electronic Engineering Handbook Series)
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