A unified treatment of models and statistical methods used in the analysis of lifetime or response time data. Draws together the most important, up-to-date methods used in engineering, medical and the biological sciences, including parametric, distribution-free, nonparametric, and graphical methods. Numerical illustrations and examples involving real data demonstrate the application of each method to problems in areas such as reliability, product performance evaluation, clinical trials, and experimentation in the biomedical sciences.
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Book Description
Editorial Reviews
Review
“...a welcome addition to the literature on survival analysis...for a unified and thorough reference of classical theory and models, this book is an excellent choice.” (Journal of the American Statistical Association, March 2004)
"This book is a role-model for other who are planning to write books…every statistician and applied researcher ought to have this book in their collection." (Journal of Statistical Computation and Simulation, October 2003)
"...expanded and updated with recent research...a valuable reference...this book...merits a place on the bookshelf of anyone concerned with the analysis of lifetime data from any field. (Technometrics, Vol. 45, No. 3, August 2003)
"...updated version of the popular text...this excellent book will serve as either a reference or a graduate-level textbook." (Short Book Reviews, Vol. 23, No. 2, August 2003)
"...excellent...provides a wealth of information for those familiar with the area." (Pharmaceutical Research, Vol. 20, No. 9, September 2003)
"...the author's aim is to cover lifetime data analysis without concentrating exclusively on any field of applications...he succeeds quite well..." (Zentralblatt Math, 2003)
“...rewritten to reflect new developments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 2, June 2003)
"Compared with the large number of other good textbooks in the this field, this is one of the best. I highly recommend that all applied statisticians add this volume to their libraries." (Applied Clinical Trials, May 2003) --This text refers to an alternate Hardcover edition.
From the Publisher
A unified treatment of models and statistical methods used in the analysis of lifetime or response time data. Draws together the most important, up-to-date methods used in engineering, medical and the biological sciences, including parametric, distribution-free, nonparametric, and graphical methods. Numerical illustrations and examples involving real data demonstrate the application of each method to problems in areas such as reliability, product performance evaluation, clinical trials, and experimentation in the biomedical sciences.
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Most Helpful Customer Reviews
27 of 27 people found the following review helpful:
5.0 out of 5 stars
one of the best texts on life data analysis,
By
This review is from: Statistical Models and Methods for Lifetime Data (Wiley Series in Probability and Statistics) (Hardcover)
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 covers multivariate models.
10 of 10 people found the following review helpful:
5.0 out of 5 stars
Excellent for Pre-Multivariate Survival Analysis,
By Loch (Seoul, Korea) - See all my reviews
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This review is from: Statistical Models and Methods for Lifetime Data (Wiley Series in Probability and Statistics) (Hardcover)
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 on reader's needs.
2.0 out of 5 stars
Not so easy to understand!,
This review is from: Statistical Models and Methods for Lifetime Data (Wiley Series in Probability and Statistics) (Hardcover)
I bought this book for "life data analysis class" (it was required). Unfortunately the book has not been as helpful as the professor and the students thought it might be. You have to go through many chapters to understand one concept, and according to my professor the second version of the book is not better than the first one.
If you are into the field of life data analysis it might be good but I don't recommend the book for someone just starting.
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Inside This Book
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First Sentence:
The statistical analysis of what is variously referred to as lifetime, survival time, or failure time data has developed into an important topic for workers in many areas, especially in the engineering and biomedical sciences. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
simple linear estimators, life test plans, standard extreme value distribution, random censorship model, permutation variance, empirical survivor function, extreme value model, censoring times, equivariant estimators, log lifetimes, standard exponential distribution, regressor variables, censored sampling, censored data, generalized gamma distribution, asymmetric alternatives, interval estimation procedures, life table estimates, concomitant variables, parametric regression models, partial likelihood function, graphical estimates, censored survival data, hazard function, pth quantile Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Cramer-von Mises, Quantile Function, Data With Several Observations New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
The statistical analysis of what is variously referred to as lifetime, survival time, or failure time data has developed into an important topic for workers in many areas, especially in the engineering and biomedical sciences. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
simple linear estimators, life test plans, standard extreme value distribution, random censorship model, permutation variance, empirical survivor function, extreme value model, censoring times, equivariant estimators, log lifetimes, standard exponential distribution, regressor variables, censored sampling, censored data, generalized gamma distribution, asymmetric alternatives, interval estimation procedures, life table estimates, concomitant variables, parametric regression models, partial likelihood function, graphical estimates, censored survival data, hazard function, pth quantile Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Cramer-von Mises, Quantile Function, Data With Several Observations New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Front Flap | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 100
books:
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100
books
cite this book:
See all 100 books citing this book
- The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics) by John D. Kalbfleisch in Back Matter (1), and Back Matter (2)
- Survival Distributions: Reliability Applications in the Biomedical Sciences (Probability & Mathematical Statistics) by Alan John Gross in Back Matter (1), and Back Matter (2)
- The Asymptotic Theory of Extreme Order Statistics by Janos Galambos in Back Matter (1), and Back Matter (2)
- Theory of Statistical Inference (Probability & Mathematical Statistics) by S. Zacks in Front Matter, and Back Matter
- Basic Statistics for Business and Economics (Management & Administration) by Paul G. Hoel in Back Matter
See all 100 books this book cites
- Applied Life Data Analysis (Wiley Series in Probability and Statistics) by Wayne B. Nelson on page 568, Back Matter (1), and Back Matter (2)
- Statistical Methods for Reliability Data (Wiley Series in Probability and Statistics) by William Q. Meeker in Back Matter (1), and Back Matter (2)
- Electronic Component Reliability: Fundamentals, Modelling, Evaluation, and Assurance (Quality and Reliability Engineering Series) by Finn Jensen on page 72, and page 99
- The Statistical Analysis of Failure Time Data (Wiley Series in Probability and Statistics) by John D. Kalbfleisch in Back Matter (1), and Back Matter (2)
- Applied Survival Analysis: Regression Modeling of Time to Event Data by David W. Hosmer Jr. in Back Matter (1), and Back Matter (2)
See all 100 books citing this book
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