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## Book Description

With an emphasis on social science applications, Event History Analysis with R presents an introduction to survival and event history analysis using real-life examples. Keeping mathematical details to a minimum, the book covers key topics, including both discrete and continuous time data, parametric proportional hazards, and accelerated failure times.

Features

• Introduces parametric proportional hazards models with baseline distributions like the Weibull, Gompertz, Lognormal, and Piecewise constant hazard distributions, in addition to traditional Cox regression
• Presents mathematical details as well as technical material in an appendix
• Includes real examples with applications in demography, econometrics, and epidemiology
• Provides a dedicated R package, eha, containing special treatments, including making cuts in the Lexis diagram, creating communal covariates, and creating period statistics

A much-needed primer, Event History Analysis with R is a didactically excellent resource for students and practitioners of applied event history and survival analysis.

1. Preliminaries
2. Preface
3. Chapter 1 Event History and Survival Data
1. 1.1 Introduction
2. 1.2 Survival Data
3. 1.3 Right Censoring
4. 1.4 Left Truncation
5. 1.5 Time Scales
6. 1.6 Event History Data
7. 1.7 More Data Sets
4. Chapter 2 Single Sample Data
1. 2.1 Introduction
2. 2.2 Continuous Time Model Descriptions
3. 2.3 Discrete Time Models
4. 2.4 Nonparametric Estimators
5. 2.5 Doing it in R
5. Chapter 3 Cox Regression
1. 3.1 Introduction
2. 3.2 Proportional Hazards
3. 3.3 The Log-Rank Test
4. 3.4 Proportional Hazards in Continuous Time
5. 3.5 Estimation of the Baseline Hazard
6. 3.6 Explanatory Variables
7. 3.7 Interactions
8. 3.8 Interpretation of Parameter Estimates
9. 3.9 Proportional Hazards in Discrete Time
10. 3.10 Model Selection
11. 3.11 Male Mortality
6. Chapter 4 Poisson Regression
1. 4.1 Introduction
2. 4.2 The Poisson Distribution
3. 4.3 The Connection to Cox Regression
4. 4.4 The Connection to the Piecewise Constant Hazards Model
7. Chapter 5 More on Cox Regression
1. 5.1 Introduction
2. 5.2 Time-Varying Covariates
3. 5.3 Communal covariates
4. 5.4 Tied Event Times
5. 5.5 Stratification
6. 5.6 Sampling of Risk Sets
7. 5.7 Residuals
8. 5.8 Checking Model Assumptions
9. 5.9 Fixed Study Period Survival
10. 5.10 Left- or Right-Censored Data
8. Chapter 6 Parametric Models
1. 6.1 Introduction
2. 6.2 Proportional Hazards Models
3. 6.3 Accelerated Failure Time Models
4. 6.4 Proportional Hazards or AFT Model?
5. 6.5 Discrete Time Models
9. Chapter 7 Multivariate Survival Models
1. 7.1 Introduction
2. 7.2 Frailty Models
3. 7.3 Parametric Frailty Models
4. 7.4 Stratification
10. Chapter 8 Competing Risks Models
1. 8.1 Introduction
2. 8.2 Some Mathematics
3. 8.3 Estimation
4. 8.4 Meaningful Probabilities
5. 8.5 Regression
6. 8.6 R Code for Competing Risks
11. Chapter 9 Causality and Matching
1. 9.1 Introduction
2. 9.2 Philosophical Aspects of Causality
3. 9.3 Causal Inference
4. 9.4 Aalen’s Additive Hazards Model
5. 9.5 Dynamic Path Analysis
6. 9.6 Matching
7. 9.7 Conclusion
12. Appendix A Basic Statistical Concepts
1. A.1 Introduction
2. A.2 Statistical Inference
1. A.2.1 Point Estimation
2. A.2.2 Interval Estimation
3. A.2.3 Hypothesis Testing
3. A.3 Asymptotic theory
4. A.4 Model Selection
13. Appendix B Survival Distributions
1. B.1 Introduction
2. B.2 Relevant Distributions in R
3. B.3 Parametric Proportional Hazards and Accelerated Failure Time Models
1. B.3.1 Introduction
2. B.3.2 The Proportional Hazards Model
3. B.3.3 The Shape-Scale Families
4. B.3.4 The Accelerated Failure Time Model
14. Appendix C A Brief Introduction to R
1. C.1 R in General
2. C.2 Some Standard R Functions
3. C.3 Writing Functions
4. C.4 Graphics
5. C.5 Probability Functions
1. C.5.1 Some Useful R Functions
6. C.6 Help in R
7. C.7 Functions in eha and survival
8. C.8 Reading Data into R
15. Appendix D Survival Packages in R
1. D.1 Introduction
2. D.2 eha
3. D.3 survival
4. D.4 Other Packages
16. Bibliography