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Modeling Survival Data: Erweiterung des Cox-Modells
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eBay-Artikelnr.:296877035192
Artikelmerkmale
- Artikelzustand
- Binding
- Hardcover
- Product Group
- Book
- Weight
- 1 lbs
- IsTextBook
- Yes
- ISBN
- 9780387987842
Über dieses Produkt
Product Identifiers
Publisher
Springer New York
ISBN-10
0387987843
ISBN-13
9780387987842
eBay Product ID (ePID)
1720881
Product Key Features
Number of Pages
Xiv, 350 Pages
Language
English
Publication Name
Modeling Survival Data : Extending the Cox Model
Subject
Biostatistics, Probability & Statistics / General, Research, Applied, Life Sciences / Biology
Publication Year
2000
Type
Textbook
Subject Area
Mathematics, Science, Medical
Series
Statistics for Biology and Health Ser.
Format
Hardcover
Dimensions
Item Height
0.3 in
Item Weight
54 Oz
Item Length
9.2 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
00-030758
Reviews
From the reviews: TECHNOMETRICS "I would be curious to know how many people bought the book by Fleming and Harrington (1991) and by Anderson, Borgan, Gill, and Keiding (1993) when they first appeared hoping for an introduction to the subject. Instead we all should have saved our money and waited fir this volume by Therneau and Grambscha? This book can serve as a useful reference for statistical practitioners who encounter survival data and for researchers who want to update their knowledge in modern survival analysisa? The writing style is light and almost humorous in many places. We get the feeling that the authors had a lot of fun writing this book. If only it was available a decade ago." STATISTICAL METHODS IN MEDICAL RESEARCH "In short, this is an exciting book, which introduces and illustrates some recent developments in surviavl analysis. The authors maintain an informal and good-humoures style, making the book very easy to read, and insist on a hands-on approach which encourages the reader to re-work examples. I would recommend it to any statistician analysing survival data." SHORT BOOK REVIEWS "The authors ... have laid out for us the wealth of their practical experience at all levels; the numerical aspects; computer algorithms; evaluation of different methods and connections between them; possible pitfalls; and interpretation of the results. Remarkable insights abound. This book completes that of P. Hougaard by giving much detail on actual fitting of the models discussed by him. It will serve two audiences: the busy practitioner who has not had time to catch up with martingale theory and counting processes and the graduate student who has just completed such a course and who needs to be introduced to the practicalities and judgements needed in data analysis. It is likely to become a well-thumbed copy on the statistician's desk and statistical practice will be the better for it." STATISTICS IN MEDICINE "I use S-PLUS in my own applied work and when testing my methodological research. Therefore, I came to this book with high expectations. I was not disappointed. The book is an invaluable resource for all researchers who use SAS and/or S-PLUS in their applied work, and who want to improve their skills in analyzing survival and event history data." JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION "a? I highly recommend [this book] to statisticians analyzing survival data with S-PLUS and SAS."
Dewey Edition
21
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
610/.7/27
Table Of Content
Estimating the Survival and Hazard Functions.- The Cox Model.- Residuals.- Functional Form.- Testing Proportional Hazards.- Influence.- Multiple Events per Subject.- Frailty Models.- Expected Survival.
Synopsis
Extending the Cox Model is aimed at researchers, practitioners, and graduate students who have some exposure to traditional methods of survival analysis. The emphasis is on semiparametric methods based on the proportional hazards model. The inclusion of examples with SAS and S-PLUS code will make the book accessible to most working statisticians., This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area. The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided. Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions. Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals., This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyze multiple/correlated event data using marginal and random effects. The focus is on actual data examples, the analysis and interpretation of results, and computation. The book shows how these new methods can be implemented in SAS and S-Plus, including computer code, worked examples, and data sets., This is a book for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Its goal is to extend the toolkit beyond the basic triad provided by most statistical packages: the Kaplan-Meier estimator, log-rank test, and Cox regression model. Building on recent developments motivated by counting process and martingale theory, it shows the reader how to extend the Cox model to analyse multiple/correlated event data using marginal and random effects (frailty) models. It covers the use of residuals and diagnostic plots to identify influential or outlying observations, assess proportional hazards and examine other aspects of goodness of fit. Other topics include time-dependent covariates and strata, discontinuous intervals of risk, multiple time scales, smoothing and regression splines, and the computation of expected survival curves. A knowledge of counting processes and martingales is not assumed as the early chapters provide an introduction to this area.The focus of the book is on actual data examples, the analysis and interpretation of the results, and computation. The methods are now readily available in SAS and S-Plus and this book gives a hands-on introduction, showing how to implement them in both packages, with worked examples for many data sets. The authors call on their extensive experience and give practical advice, including pitfalls to be avoided.Terry Therneau is Head of the Section of Biostatistics, Mayo Clinic, Rochester, Minnesota. He is actively involved in medical consulting, with emphasis in the areas of chronic liver disease, physical medicine, hematology, and laboratory medicine, and is an author on numerous papers in medical and statistical journals. He wrote two of the original SAS procedures for survival analysis (coxregr and survtest), as well as the majority of the S-Plus survival functions.Patricia Grambsch is Associate Professor in the Division of Biostatistics, School of Public Health, University of Minnesota. She has collaborated extensively with physicians and public health researchers in chronic liver disease, cancer prevention, hypertension clinical trials and psychiatric research. She is a fellow the American Statistical Association and the author of many papers in medical and statistical journals., This book presents some of the recent developments in survival methods, along with the appropriate code in S-PLUS and SAS so they can be implemented. The book will be of interest to researchers, practitioners, and graduate students working in the areas of biostatistics, epidemilogy, and medical research.
LC Classification Number
QA273.A1-274.9
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- e***r (4062)- Bewertung vom Käufer.Letzter MonatBestätigter KaufGood fast business. Thanks. A#1!
- b***r (155)- Bewertung vom Käufer.Letzter MonatBestätigter KaufRare book in great condition. Good quality, looks great. Very happy that seller offered this item at great value.
- e***w (1492)- Bewertung vom Käufer.Letzter MonatBestätigter KaufItem in as new condition. Quick shipping. Great seller, buy with confidence!