|
An Introduction to Survival Analysis Using Stata, Second Edition is
the ideal tutorial for professional data analysts who want to learn survival
analysis for the first time or who are well versed in survival analysis but
not as dexterous in using Stata to analyze survival data. This text also
serves as a valuable reference to those who already have experience using
Stata’s survival analysis routines.
The second edition has been updated for Stata 10, containing a new chapter
on power and sample-size calculations for survival studies and sections that
describe how to fit regression models (stcox and streg) in the
presence of complex survey data. Other enhancements include discussions
about nonparametric estimation of mean/median survival, survival graphs with
embedded at-risk tables, better hazard graphs through the use of boundary
kernels, and concordance measures for assessing the predictive accuracy of
the Cox model, as well as an expanded discussion of model building
strategies including the use of fractional polynomials.
Survival analysis is a field of its own requiring specialized data
management and analysis procedures. Toward this end, Stata provides the
st family of commands for organizing and summarizing survival data.
The authors of this text are also the authors of Stata’s st
commands.
This book provides statistical theory, step-by-step procedures for analyzing
survival data, an in-depth usage guide for Stata’s most widely used
st commands, and a collection of tips for using Stata to analyze
survival data and present the results. This book develops from first
principles the statistical concepts unique to survival data and assumes only
a knowledge of basic probability and statistics and a working knowledge of
Stata.
The first three chapters of the text cover basic theoretical concepts:
hazard functions, cumulative hazard functions, and their interpretations;
survivor functions; hazard models; and a comparison of nonparametric,
semiparametric, and parametric methodologies. Chapter 4 deals with censoring
and truncation. The next three chapters cover the formatting, manipulation,
stsetting, and error checking involved in preparing survival data for
analysis using Stata’s st analysis commands. Chapter 8 covers
nonparametric methods, including the Kaplan–Meier and
Nelson–Aalen estimators, and the various nonparametric tests for the
equality of survival experience.
Chapters 9–11 discuss Cox regression and include various examples of
fitting a Cox model, obtaining predictions, interpreting results, building
models, and model diagnostics. The next four chapters cover parametric
models, which are fit using Stata’s streg command. These
chapters include detailed derivations of all six parametric models currently
supported in Stata and methods for determining which model is appropriate,
as well as information on obtaining predictions, stratification, and
advanced topics such as frailty models. The final chapter is devoted to
power and sample-size calculations for survival studies.
For further details or to order online, please visit the
Stata Bookstore.
|
