TA的每日心情  开心 2015217 10:44 

签到天数: 1 天 [LV.1]初来乍到

This is the second edition of this text on survival analysis,
originally published in 1996. As in the first edition, each chapter
contains a presentation of its topic in “lecturebook” format
together with objectives, an outline, key formulae, practice
exercises, and a test. The “lecturebook” format has a
sequence of illustrations and formulae in the left column of
each page and a script in the right column. This format allows
you to read the script in conjunction with the illustrations and
formulae that highlight the main points, formulae, or examples
being presented.
This second edition has expanded the first edition by adding
three new chapters and a revised computer appendix. The
three new chapters are:
Chapter 7. Parametric Survival Models
Chapter 8. Recurrent Event Survival Analysis
Chapter 9. Competing Risks Survival Analysis
Chapter 7 extends survival analysis methods to a class of survival
models, called parametric models, in which the distribution
of the outcome (i.e., the time to event) is specified in
terms of unknown parameters. Many such parametric models
are acceleration failure time models, which provide an alternative
measure to the hazard ratio called the “acceleration
factor”. The general form of the likelihood for a parametric
model that allows for left, right, or interval censored data is
also described. The chapter concludes with an introduction
to frailty models.
Chapter 8 considers survival events that may occur more than
once over the followup time for a given subject. Such events
are called “recurrent events”. Analysis of such data can be
carried out using a Cox PH model with the data layout augmented
so that each subject has a line of data for each recurrent
event. A variation of this approach uses a stratified
Cox PH model, which stratifies on the order in which recurrent
events occur. The use of “robust variance estimates” are
recommended to adjust the variances of estimated model coefficients
for correlation among recurrent events on the same
subject.
viii Preface
Chapter 9 considers survival data in which each subject can
experience only one of several different types of events (“competing
risks”) over followup. Modeling such data can be carried
out using a Cox model, a parametric survival model or a
model which uses cumulative incidence (rather than survival).
The Computer Appendix in the first edition of this text has
now been revised and extended to provide stepbystep instructions
for using the computer packages STATA (version
7.0), SAS (version 8.2), and SPSS (version 11.5) to carry out
the survival analyses presented in the main text. These computer
packages are described in separate selfcontained sections
of the Computer Appendix, with the analysis of the same
datasets illustrated in each section. The SPIDA package used
in the first edition is no longer active and has therefore been
omitted from the appendix and computer output in the main
text.
In addition to the above new material, the original six chapters
have been modified slightly to correct for errata in the first
edition, to clarify certain issues, and to add theoretical background,
particularly regarding the formulation of the (partial)
likelihood functions for the Cox PH (Chapter 3) and extended
Cox (Chapter 6) models.
The authors’ website for this textbook has the following weblink:
http://www.sph.emory.edu/∼dkleinb/surv2.htm
This website includes information on how to order this
second edition from the publisher and a freely downloadable
zipfile containing datafiles for examples used in the textbook.
Suggestions
for Use
This text was originally intended for selfstudy, but in the nine
years since the first edition was published, it has also been effectively
used as a text in a standard lecturetype classroom
format. The text may also be use to supplement material covered
in a course or to review previously learned material in
a selfinstructional course or selfplanned learning activity.
A more individualized learning program may be particularly
suitable to a working professional who does not have the time
to participate in a regularly scheduled course.
Preface ix
In working with any chapter, the learner is encouraged first to
read the abbreviated outline and the objectives and then work
through the presentation. The reader is then encouraged to
read the detailed outline for a summary of the presentation,
work through the practice exercises, and, finally, complete the
test to check what has been learned.
Recommended
Preparation
The ideal preparation for this text on survival analysis is a
course on quantitative methods in epidemiology and a course
in applied multiple regression. Also, knowledge of logistic regression,
modeling strategies, and maximum likelihood techniques
is crucial for the material on the Cox and parametric
models described in chapters 3–9.
Recommended references on these subjects, with suggested
chapter readings are:
Kleinbaum D, Kupper L, Muller K, and Nizam A, Applied
Regression Analysis and Other Multivariable Methods,
Third Edition, Duxbury Press, Pacific Grove, 1998, Chapters
1–16, 22–23
Kleinbaum D, Kupper L and Morgenstern H, Epidemiologic
Research: Principles and Quantitative Methods, John
Wiley and Sons, Publishers, New York, 1982, Chapters 20–
24.
Kleinbaum D and Klein M, Logistic Regression: A Self
Learning Text, Second Edition, SpringerVerlag Publishers,
New York, Chapters 4–7, 11.
Kleinbaum D, ActivEpiA CD Rom Electronic Textbook on
Fundamentals of Epidemiology, SpringerVerlag Publishers,
New York, 2002, Chapters 13–15.
A first course on the principles of epidemiologic research
would be helpful, since all chapters in this text are written
from the perspective of epidemiologic research. In particular,
the reader should be familiar with the basic characteristics of
epidemiologic study designs, and should have some idea of
the frequently encountered problem of controlling for confounding
and assessing interaction/effect modification. The
above reference, ActivEpi, provides a convenient and hopefully
enjoyable way to review epidemiology.

