Since publication of the first edition nearly a decade ago,
analyses using time-to-event methods have increase considerably in
all areas of scientific inquiry mainly as a result of
model-building methods available in modern statistical software
packages. However, there has been minimal coverage in the available
literature to9 guide researchers, practitioners, and students who
wish to apply these methods to health-related areas of study.
Applied Survival Analysis, Second Edition provides a comprehensive
and up-to-date introduction to regression modeling for
time-to-event data in medical, epidemiological, biostatistical, and
other health-related research.

This book places a unique emphasis on the practical and
contemporary applications of regression modeling rather than the
mathematical theory. It offers a clear and accessible presentation
of modern modeling techniques supplemented with real-world examples
and case studies. Key topics covered include: variable selection,
identification of the scale of continuous covariates, the role of
interactions in the model, assessment of fit and model assumptions,
regression diagnostics, recurrent event models, frailty models,
additive models, competing risk models, and missing data.

Features of the Second Edition include:

Expanded coverage of interactions and the covariate-adjusted
survival functions
The use of the Worchester Heart Attack Study as the main
modeling data set for illustrating discussed concepts and
techniques
New discussion of variable selection with multivariable
fractional polynomials
Further exploration of time-varying covariates, complex with
examples
Additional treatment of the exponential, Weibull, and
log-logistic parametric regression models
Increased emphasis on interpreting and using results as well as
utilizing multiple imputation methods to analyze data with missing
values
New examples and exercises at the end of each chapter

Analyses throughout the text are performed using Stata®
Version 9, and an accompanying FTP site contains the data sets used
in the book. Applied Survival Analysis, Second Edition is an ideal
book for graduate-level courses in biostatistics, statistics, and
epidemiologic methods. It also serves as a valuable reference for
practitioners and researchers in any health-related field or for
professionals in insurance and government.

Auteur

David W. Hosmer, PhD, is Professor Emeritus of Biostatistics
in the School of Public Health and Heatlth Sciences at the
University of Massachusetts Amherst. Dr. Hosmer is the coauthor of
Applied Logistic Regression, published by Wiley.

Stanley Lemeshow, PhD, is Professor and Dean of the
College of Public Health at The Ohio State University. Dr. Lemeshow
has over thirty-five years of academic experience in the areas of
regression, categorical data methods, and sampling methods. He is
the coauthor of Sampling of Population: Methods and
Application and Applied Logistic Regression, both
published by Wiley.

Susanne May, PhD, is Assistant Professor of Biostatistics
at the University of California, San Diego. Dr. May has over twelve
years of experience in providing statistical support for
health-related research projects.

Résumé
THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATANOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research.

This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data.

Features of the Second Edition include:

Expanded coverage of interactions and the covariate-adjusted survival functions
The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques
New discussion of variable selection with multivariable fractional polynomials
Further exploration of time-varying covariates, complex with examples
Additional treatment of the exponential, Weibull, and log-logistic parametric regression models
Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values
New examples and exercises at the end of each chapter
Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

Contenu

Preface xi