» Analysis of Multivariate Survival Data
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Rating: 5.0 / 5.00 (2 reviews)
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Manufacturer: Springer
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Rating: 5.0 / 5.00 (2 reviews)
Usually ships in 24 hours
Manufacturer: Springer
Click to Buy
Analysis of Multivariate Survival Data Details
Binding: HardcoverDewey Decimal Number: 610.727
EAN: 9780387988733
ISBN: 0387988734
Label: Springer
Manufacturer: Springer
Number Of Items: 1
Number Of Pages: 560
Publication Date: 2001-11-30
Publisher: Springer
Studio: Springer
Accessories for Analysis of Multivariate Survival Data
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Analysis of Multivariate Survival Data Reviews
Customer Rating:Summary: first and excellent treatment of a key topic in pharmaceuticals multivariate survival
Comment: The author is one of the pioneers in the newly developing field of multivariate survival analysis. His work goes back to his Ph.D. dissertation in the mid 1980s. These methods come into play when one is studying more than one survival curve and the event times are correlated rather than independent. Practical applications include situations when multiple events are studied on the same patients, such as time until contracting the disease, followed by time to complications and then possibly by time to death from the disease. Studies involving events related to twins can also be analyzed by these methods.
He gives an excellent exposition and a number of good examples. He provides the reader with a very current list of references from the literature.
The author presents the four common approaches to the problem and concedes that the field is in its infancy. He believes that while some of the methods described will prove not to be as fruitful as others, at this point it is still difficult to determine which are the most promising. His aim is to expand the toolbox for researchers in medical and biological fields who have experience with univariate survival analysis and may be faced with multivariate problems. He covers such important current topics as fraility models and competing risks.
In my opinion the author has succeeded in his goal and provided biostatisticians with a reference source that will be useful to them for many years. It should not be your first book in survival analysis though. See the book by Lawless or Kalbfleish and Prentice before attaching this book.
Customer Rating:
Summary: first book on multivariate survival analysis
Comment: The author is one of the pioneers in the newly developing field of multivariate survival analysis. His work goes back to his Ph.D. dissertation in the mid 1980s. These methods come into play when one is studying more than one survival curve and the event times are correlated rather than independent. Practical applications include situations when multiple events are studied on the same patients, such as time until contracting the disease, followed by time to complications and then possibly by time to death from the disease. Studies involving events related to twins can also be analyzed by these methods.
He gives an excellent exposition and a number of good examples. He provides the reader with a very current list of references from the literature.
The author presents the four common approaches to the problem and concedes that the field is in its infancy. He believes that while some of the methods described will prove not to be as fruitful as others, at this point it is still difficult to determine which are the most promising. His aim is to expand the toolbox for researchers in medical and biological fields who have experience with univariate survival analysis and may be faced with multivariate problems. He covers such important current topics as fraility models and competing risks.
In my opinion the author has succeeded in his goal and provided biostatisticians with a reference source that will be useful to them for many years. It should not be your first book in survival analysis though. See the book by Lawless or Kalbfleish and Prentice before attaching this book.
Editorial Review for Analysis of Multivariate Survival Data:
Survival data or more general time-to-event data occur in many areas, including medicine, biology, engineering, economics, and demography, but previously standard methods have requested that all time variables are univariate and independent. This book extends the field by allowing for multivariate times. Applications where such data appear are survival of twins, survival of married couples and families, time to failure of right and left kidney for diabetic patients, life history data with time to outbreak of disease, complications and death, recurrent episodes of diseases and cross-over studies with time responses. As the field is rather new, the concepts and the possible types of data are described in detail and basic aspects of how dependence can appear in such data is discussed. Four different approaches to the analysis of such data are presented. The multi-state models where a life history is described as the subject moving from state to state is the most classical approach. The Markov models make up an important special case, but it is also described how easily more general models are set up and analyzed. Frailty models, which are random effects models for survival data, made a second approach, extending from the most simple shared frailty models, which are considered in detail, to models with more complicated dependence structures over individuals or over time. Marginal modelling has become a popular approach to evaluate the effect of explanatory factors in the presence of dependence, but without having specified a statistical model for the dependence. Finally, the completely non-parametric approach to bivariate censored survival data is described. This book is aimed at investigators who need to analyze multivariate survival data, but due to its focus on the concepts and the modelling aspects, it is also useful for persons interested in such data, but not having a statistical education. It can be used as a textbook for a graduate course in multivariate survival data. It is made from an applied point of view and covers all essential aspects of applying multivariate survival models. Also more theoretical evaluations, like asymptotic theory, are described, but only to the extent useful in applications and for understanding the models. For reading the book, it is useful, but not necessary, to have an understanding of univariate survival data. Philip Hougaard is a statistician at the pharmaceutical company Novo Nordisk. He has a Ph.D. in nonlinear regression models and is Doctor of Science based on a thesis on frailty models. He is associate editor of Biometrics and Lifetime Data Analysis. He has published over 80 papers in the statistical and medical literature.