Applied Survival Analysis: Regression Modeling of Time to Event Data / Edition 2

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This book provides a comprehensive, self-contained introduction to regression modeling used in the analysis of time-to-event data in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and it offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies.

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Editorial Reviews

From the Publisher
“This is a great book for anyone analyzing time-to-event data.  Researchers interested in the underlying theory will have to go elsewhere..”  (Stat Papers, 1 December 2012)

"It is well suited for teaching a graduate-level course in medical statistics, and the data sets used in the book are available online." (Biometrical Journal, August 2009)

"This is a superb resource - a practical guide with up-to-date applications. The authors are excellent teachers of the mathematics and application of survival data regression modeling." (Doodys, August 2009)

"The extensive and detailed coverage of the process of survival model fitting, as well as the applied exercises, make this textbook an excellent choice for an applied survival analysis course." (Journal of Biopharmaceutical Statistics, Volume 18, Issue 6, 2008)

Doody's Review Service
Reviewer: Sharon M. Homan, PhD (Kansas Health Institute)
Description: Since the publication of the first edition of this book in 1999, there have been significant advances in statistical methods and software applications to analyzing survival data in medical, epidemiological, and other health-related research. The approach taken in this second edition is similar to that in the first edition, but it expands coverage of interactions and covariate-adjusted survival functions, variable selection, time-dependent covariates, parametric regression models, competing risk models, and missing data methods.
Purpose: The authors emphasize practical and contemporary applications of survival data regression modeling. Their goal is to "provide a focused text on regression modeling for the time-to-event data typically encountered in health related studies." Researchers and analysts increasingly work with time-to-event data, and have many more software options to perform analyses. Choosing among various modeling and software options is critical to developing a good analysis plan and conducting optimal analyses. This book is a great resource, with its clear and accessible presentation of modeling techniques, case studies, and practical applications.
Audience: It is intended as a textbook for graduate courses in biostatistics, statistics, and epidemiologic methods, and as a reference for practitioners and researchers in health-related fields who have a foundation in linear and logistic regression methods. The authors are highly regarded biostatisticians, with enormous expertise in the development, application, and teaching of survival data analysis methods.
Features: The book begins by building a mathematically sound and practical foundation of survival data, survival time functions, rand egression modeling. Next, the proportional hazards model is developed - estimation, model fitting, and model adequacy. Extensions of the proportional hazards model include stratified models, time-varying covariates, truncation and censoring. The final chapters cover parametric modeling, recurrent event and frailty models, and competing risk models. One of the best features of this book is the presentation of well-developed applications and excellent examples and illustrations of analyses using Stata 9.0 and SAS 9.1. Challenging exercises end each chapter, but notes and/or solutions are not provided. Data sets can be downloaded from the publisher's web site. Adding the programming code for the examples and chapter exercises in an appendix on the web site would be even better.
Assessment: This is a superb resource - a practical guide with up-to-date applications. The authors are excellent teachers of the mathematics and application of survival data regression modeling including how to handle complexities such as time-varying covariates and correlated observations. The second edition is a significant revision, incorporating the new capabilities of Stata, easy-to-use software with good graphical and statistical analysis capabilities, and introducing contemporary applications of regression modeling of time-to-event data.
This is actually a great book to read. It has a wealth of examples and applications.
This is actually a great book to read. It has a wealth of examples and applications.
International Journal of Epidemiology
...the book is an ideal textbook for people with knowledge of regression analysis who want to become acquainted with the methods of survival analysis.
Statistical Methods in Medical Research
...highly recommended...
A textbook for an introductory course in statistical methods for analyzing data typically encountered in health related studies that include events involving an element of time. Assumes previous courses in linear and logical regression. Emphasizes practical applications rather than mathematical theory, modeling data, and interpreting results. Also highlights the importance of incomplete or censored data and how that censoring may influence the selection of models and the interpretation of results. Mostly uses examples from STATA, but the methods are fairly ubiquitous among the currently available statistical software packages. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9780471754992
  • Publisher: Wiley
  • Publication date: 1/18/2008
  • Series: Wiley Series in Probability and Statistics Series, #618
  • Edition description: New Edition
  • Edition number: 2
  • Pages: 416
  • Sales rank: 637,968
  • Product dimensions: 6.26 (w) x 9.33 (h) x 1.10 (d)

Meet the Author

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.

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Table of Contents


1. Introduction to Regression Modeling of Survival Data.

1.1 Introduction.

1.2 Typical Censoring Mechanisms.

1.3 Example Data Sets.


2. Descriptive Methods for Survival Data.

2.1 Introduction.

2.2 Estimating the Survival Function.

2.3 Using the Estimated Survival Function.

2.4 Comparison of Survival Functions.

2.5 Other Functions of Survival Time and Their Estimators.


3. Regression Models for Survival Data.

3.1 Introduction.

3.2 Semi-Parametric Regression Models.

3.3 Fitting the Proportional Hazards Regression Model.

3.4 Fitting the Proportional Hazards Model with Tied Survival Times.

3.5 Estimating the Survival Function of the Proportional Hazards Regression Model.


4. Interpretation of a Fitted Proportional Hazards Regression Model.

4.1 Introduction.

4.2 Nominal Scale Covariate.

4.3 Continuous Scale Covariate.

4.4 Multiple-Covariate Models.

4.5 Interpreting and Using the Estimated Covariate-Adjusted Survival Function.


5. Model Development.

5.1 Introduction.

5.2 Purposeful Selection of Covariates.

5.2.1 Methods to examine the scale of continuous covariates in the log hazard.

5.2.2 An example of purposeful selection of covariates.

5.3 Stepwise, Best-Subsets and Multivariable Fractional Polynomial Methods of Selecting Covariates.

5.3.1 Stepwise selection of covariates.

5.3.2 Best subsets selection of covariates.

5.3.3 Selecting covariates and checking their scale using multivariable fractional polynomials.

5.4 Numerical Problems.


6. Assessment of Model Adequacy.

6.1 Introduction.

6.2 Residuals.

6.3 Assessing the Proportional Hazards Assumption.

6.4 Identification of Influential and Poorly Fit Subjects.

6.5 Assessing Overall Goodness-of-Fit.

6.6 Interpreting and Presenting Results From the Final Model.


7. Extensions of the Proportional Hazards Model.

7.1 Introduction.

7.2 The Stratified Proportional Hazards Model.

7.3 Time-Varying Covariates.

7.4 Truncated, Left Censored and Interval Censored Data.


8. Parametric Regression Models.

8.1 Introduction.

8.2 The Exponential Regression Model.

8.3 The Weibull Regression Model.

8.4 The Log-Logistic Regression Model.

8.5 Other Parametric Regression Models.


9. Other Models and Topics.

9.1 Introduction.

9.2 Recurrent Event Models.

9.3 Frailty Models.

9.4 Nested Case-Control Studies.

9.5 Additive Models.

9.6 Competing Risk Models.

9.7 Sample Size and Power.

9.8 Missing Data.


Appendix 1: The Delta Method.

Appendix 2: An Introduction to the Counting Process Approach to Survival Analysis.

Appendix 3: Percentiles for Computation of the Hall and Wellner Confidence Band.



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