- 548 pages
- English
- PDF
- Available on iOS & Android
eBook - PDF
Modelling Survival Data in Medical Research
About this book
Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range of examples from biomedical research.Well known for its nontechnical style, this third edition contains new chapters on frailty models and their applications, competing risks, non-proportional hazards, and dependent censo
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Yes, you can access Modelling Survival Data in Medical Research by David Collett in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.
Information
16
SURVIVAL
ANALYSIS
The
use
of
S-PLUS
in
survival
analysis
is
also
described
in
Everitt
and
Rabe-
Hesketh
(2001)
and
Tableman
and
Kim
(2004),
while
Brostr¨om
(2012)
shows
how
R
is
used
in
the
analysis
of
survival
data.
Venables
and
Ripley
(2002)
describe
how
graphical
and
numerical
data
analyses
can
be
carried
out
in
the
S
environment
that
is
implemented
in
both
R
and
S-PLUS;
note
that
S
code
generally
runs
under
R.
A
similarly
comprehensive
account
of
the
R
system
is
given
by
Crawley
(2013),
while
Dalgaard
(2008)
gives
a
more
elementary
introduction
to
R.
The
short
introduction
to
R
of
Venables
and
Smith
(2009)
is
also
available
from
R
Core
Team
(2013).
The
use
of
Stata
in
survival
analysis
is
presented
by
Cleves
et
al.
(2010),
and
Rabe-Hesketh
and
Everitt
(2007)
give
a
more
general
introduction
to
the
use
of
Stata
in
data
analysis.
Table of contents
- Front Cover
- Contents
- Preface
- Chapter 1: Survival analysis
- Chapter 2: Some non-parametric procedures
- Chapter 3: The Cox regression model
- Chapter 4: Model checking in the Cox regression model
- Chapter 5: Parametric proportional hazards models
- Chapter 6: Accelerated failure time and other parametric models
- Chapter 7: Model checking in parametric models
- Chapter 8: Time-dependent variables
- Chapter 9: Interval-censored survival data
- Chapter 10: Frailty models
- Chapter 11: Non-proportional hazards and institutional comparisons
- Chapter 12: Competing risks
- Chapter 13: Multiple events and event history modelling
- Chapter 14: Dependent censoring
- Chapter 15: Sample size requirements for a survival study
- Appendix A: Maximum likelihood estimation
- Appendix B: Additional data sets
- Bibliography
- Back Cover