Event History and Survival Analysis, Second Edition
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Comment from the Stata technical group
Event History and Survival Analysis, Second Edition is a concise yet substantive book that discusses the main techniques currently used for modeling survival analysis. Mathematical formulas have been kept to a minimum throughout the book and mostly relegated to an appendix. Instead, the book focuses on the fundamental concepts; for example, you will find a discussion on the different kinds of censoring and on how to perform sensitivity analysis for the noninformative assumption.
The book starts by discussing models for discrete-time survival analysis. Then it moves on to models for continuous time, first addressing parametric models and then the Cox proportional hazards model. Finally, it discusses more advanced topics such as multiple types of events, which include competing risks and repeated events.
The author guides the readers through these models by explaining different examples from the social sciences. Stata (and SAS) code to reproduce those examples, as well as the datasets used in the book, are available online.
This book constitutes an excellent resource for learning survival analysis, and we also recommend it as a reference for any researcher who works with event history data.
Table of contents
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About the Author
Series Editor's Introduction John Fox
Preface to the Second Edition
Problems in the Analysis of Event Histories
An Overview of Event History Methods
2. Discrete-Time Methods
A Discrete-Time Example
The Discrete-Time Hazard
A Logistic Regression Model
Estimating the Model
Estimates for the Biochemistry Example
The Likelihood-Ratio Chi-Square Test
Issues With the Discrete-Time Logistic Method
Discrete Versus Continuous Time
3. Parametric Methods for Continuous-Time Data
The Continuous-Time Hazard Rate
Parametric Proportional Hazards Method
Maximum Likelihood Estimation
An Empirical Example
Accelerated Failure Time Models
Evaluating Model Fit
Unobserved Sources of Heterogeneity
Why Parametric Models?
4. Cox Regression
The Proportional Hazards Model
Partial Likelihood Applied to the Recidivism Data
Time-Varying Explanatory Variables
Testing and Relaxing the Proportional Hazards Assumption
Choice of Origin for the Measurement of Time
Cox Regression for Discrete-Time Data
Predictions Based on the Cox Model
5. Multiple Kinds of Events
A Classification of Multiple Kinds of Events
Estimation for Parallel Processes
Models for Competing Risks
An Empirical Example of Competing Risks
Dependence Among Different Kinds of Events
Cumulative Incidence Functions
6. Repeated Events
Count Data Models for Repeated Events
Methods Based on Gap Times
Methods Based on Times Since Origin
Discrete-Time Likelihood Function for Non-Repeated Events