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About the Book

This book is for anyone who wants to understand the basics of survival and event history analysis and apply these methods without getting entangled in mathematical and theoretical technicalities. Readers are offered a blueprint for their entire research project from data preparation to model selection and diagnostics.

 

Engaging, easy to read, functional and packed with enlightening examples, ‘hands-on’ exercises, and resources for both students and instructors, Introducing Survival and Event History Analysis allows researchers to quickly master advanced statistical techniques. Moreover, this book is written from the perspective of the ‘user’, making it suitable as both a self-learning tool and graduate-level textbook.

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Introducing Survival and Event History Analysis covers up-to-date innovations in the field, including advancements in the assessment of model fit, frailty and recurrent events, discrete-time, multistate models and sequence analysis. Practical instructions are also included for using the statistical programs of R and STATA, enabling readers to replicate the examples described in the text.

Key coverage includes

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  • Data structures and preparation

  • Kaplan-Meier estimation, Cox and parametric regression models,

  • Model development, selection and diagnostics

  • Inclusion of time-varying and lagged covariates

  • Competing risk and multistate models

  • Discrete-time models

  • Multilevel frailty models

  • Competing risk and multistate models

  • Sequence analysis of entire histories

  • A range of supportive materials to download at the companion website, including data sets and programming files

  • Further study exercises and teaching material

  • An introduction to R for newcomers

Table of Contents

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  1. The fundamentals of survival and event history analysis

  2. An introduction to R and data exploration via descriptive

  3. Survival and event history data structures

  4. Non-parametric methods: the Kaplan Meier estimator

  5. The Cox proportional-hazards regression model

  6. Parametric models

  7. Model-building and diagnostics

  8. Frailty and recurrent event models

  9. Discrete-time models

  10. Competing risk and multistate models

  11. Sequence analysis

  12. Appendix 1: Description of the data used in this book

  13. Appendix 2: Survival and event history analysis using stata

​Download the data files used in the book

The data files used in the book are generally part of the existing libraries in R. A description of how to access data files that are part of an existing library package is described in detail in the book. See section 2.8.4 on page 34. For a complete description of all data files, refer to Appendix 1: Description of the data used in this book (on page 227)

 

Some of the data used in the book is shown here. This page will be updated with additional examples and data in the future.

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Data for R

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  1. Rossi data

  2. Rossi long data

  3. addicts data

  4. leukemia data

 

Data for Stata

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  1. Rossi data

  2. addicts data

  3. leukemia data

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Script and do Files

The Stata do files are shown in the Appendix 2: Survival and event history analysis using Stata (on page 232), where most of the analyses in the book are replicated in Stata.

 

Chapter 1: The fundamentals of survival and event history analysis (no script files)
Chapter 2: An introduction to R and data exploration via descriptive statistics and graphics
Chapter 3: Survival and event history data structures
Chapter 4: Non-parametric methods: the Kaplan–Meier estimator
Chapter 5: The Cox proportional-hazards regression model
Chapter 6: Parametric models
Chapter 7: Model-building and diagnostics

Chapter 8: Frailty and recurrent event models
Chapter 9:  Discrete-time models
Chapter 10: Competing risk and multi-state models
Chapter 11: Sequence analysis
Appendix 2: Survival and event history analysis using Stata

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Data restructuring

Chapter 3 in the book describes different types of data and data restructuring for:

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  • Examples of different survival and event history data formats

  • Converting single-episode to multi-episode data

  • Creating subject-period or discrete-time data

  • Episode-splitting

  • Converting Date formats

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