PDF Download Statistical Methods for Survival Data Analysis
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Statistical Methods for Survival Data Analysis
PDF Download Statistical Methods for Survival Data Analysis
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Review
"…an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject." (Statistics in Medical Research, October 2005) "The book is well written and provides explicit details of the models and methods used." (Journal of the American Statistical Association, June 2004) "…contains thorough descriptions and illustrations of several useful nonparametric and parametric statistical methods to analyze survival data." (Journal of Statistical Computation & Simulation, May 2004) "...a comprehensive and clearly written textbook...a very good reference..." (Short Book Reviews, Vol 24(1), 2004) "...continues to be application-oriented with a minimum level of mathematics..." (Zentralblatt Math, Vol 1026, 2004) "This third edition is a significant upgrade...this remains an excellent choice or a basic textbook in survival analysis..." (Technometrics, Vol. 45, No. 4, November 2003)
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From the Back Cover
The Third Edition of the leading reference on survival data analysis The study of survival data attempts to predict the probability of response, survival, or mean lifetime; compare; the survival distributions of experimental animals or of human patients; and identify risk and/or prognosis factors. Statistical Methods for Survival Data Analysis, Third Edition examines the statistical methods for analyzing survival data from laboratory studies of animals, clinical and epidemiological studies of humans, and other appropriate applications. Emphasizing applications over rigorous mathematics, this extremely useful reference provides thorough discussions of the most commonly used parametric and nonparametric methods in survival analysis, as well as guidelines for the planning and design of clinical trials. The authors give special consideration to the study of survival data in biomedical sciences, though the methods are suitable for applications in industrial reliability, the social sciences, and business. This Third Edition brings this standard in the field up to date with new material and revised references including: A new introduction to left and interval censored data The generalized gamma and log-logistic distribution Estimation procedures for left and interval censored data Parametric models with covariates Cox’s proportional hazards model including stratification and time-dependent covariates, and some non-proportional hazards models Goodness-of-Fit tests and model selection methods Multiple responses to the logistic regression model Numerous real-life examples which illustrate key concepts Computer programming codes in SAS, BMDP, and SPSS for most examples Related FTP site providing large data sets These additions and revisions make Statistical Methods for Survival Data Analysis, Third Edition, more valuable than ever as an essential reference for biomedical investigators, statisticians, epidemiologists, and researchers in other disciplines involved or interested in the analysis of survival data.
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Product details
Hardcover: 534 pages
Publisher: Wiley-Interscience; 3 edition (April 17, 2003)
Language: English
ISBN-10: 0471369977
ISBN-13: 978-0471369974
Product Dimensions:
6.4 x 1.3 x 9.3 inches
Shipping Weight: 1.9 pounds
Average Customer Review:
5.0 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#450,302 in Books (See Top 100 in Books)
The book came in excellent condition and is a good resource for Survival Analysis. It is a good and straightforward read and would definitely buy it again.
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.
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