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Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition

David W. Hosmer, Jr., Stanley Lemeshow, and Susanne May
Publisher: Wiley
Copyright: 2008
ISBN-13: 978-0-471-75499-2
Pages: 416; hardcover
Price: $98.50
Supplements:data (from Wiley ftp site)

Comment from the Stata technical group

Applied Survival Analysis: Regression Modeling of Time to Event Data, Second Edition, by David W. Hosmer Jr., Stanley Lemeshow, and Susanne May, is an ideal choice for a semester-long course in survival analysis for health professionals. The authors provide a good overview of regression models for time-to-event data, giving the most depth to the Cox proportional hazards model.

Unlike similar texts, Applied Survival Analysis is not overly abstract or mathematical in its introduction of the concepts of survival analysis, but it instead relies on a model-building approach. As such, this text is most useful to those who are experienced in using regression models in nonsurvival settings, such as Gaussian or logistic regression. The text builds upon the reader's prior experience by showing how the usual techniques of regression and model building apply to survival data.

The text illustrates most of its analyses in Stata, and material added since the first edition mirrors Stata's growth in survival analysis, for example, the new material on multivariable fractional polynomials and frailty models.

The authors cover such topics as censoring; descriptive methods such as Kaplan–Meier; the Cox model (including estimation, model building, diagnostics, and extensions); parametric regression models (an introductory chapter); and some advanced topics, such as recurrent event models and competing risks.

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 Interpretating 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 Interpretating and Presentating 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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