Introduction. Nonparametric Methods. Parametric Methods. Regression Models. The Cox Proportional Hazards Model. Model Checking: Data Diagnostics. Additional Topics. Censored Regression Quantiles. References.
Mara Tableman, Jong Sung Kim
"All in all the book succeeds nicely in getting the reader through
the basic methods of survival analysis (Kaplan-Meier, log-rank,
Weibull and Cox regression) and how to implement them in S."
-Journal of Statistical Software, Vol. 11, July 2004
"There are many books on survival analysis, so an obvious question
is what makes this one any different …? The main answers are the
well-integrated S code that is used throughout the book and a
chapter on censored regression quantiles … . [T]he topics that are
covered … provide the reader with a good grasp of the principles of
analysing survival data and the writing style is clear and easy to
follow. I recommend this book for anyone who wants a good
introduction to practical survival analysis using S."
-Journal of the Royal Statistics Society, Issue 167(4)
"This book introduces the field of survival analysis in a concise,
coherent manner that capture the spirit of the methods without
getting too embroiled in theoretical technicalities…this
well-written book would be an excellent choice for a textbook for a
course in survival analysis."
-Zentralblatt MATH 104
"This well-written book would be an excellent choice for a textbook
for a course in survival analysis. All of the usual topics for a
course in survival analysis are covered, including a careful
discussion of parametric models. The explanations are clear and
concise. The book not only teaches about the statistical methods
for survival analysis, but also provides detailed instruction on
how to do the computations with S-PLUS or R at a level where
students will become proficient with the S language. The book
contains an excellent collection of exercises. These exercises have
been usefully partitioned into applications and questions that ask
students to use their knowledge of probability and mathematical
statistics."
-William Q Meeker, Distinguished Professor in the Department of
Statistics, Iowa State University
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