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.


  • 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.

chapter 1|16 pages

Event History and Survival Data

chapter 2|14 pages

Single Sample Data

chapter 3|26 pages

Cox Regression

chapter 4|10 pages

Poisson Regression

chapter 5|18 pages

More on Cox Regression

chapter 6|42 pages

Parametric Models

chapter 7|12 pages

Multivariate Survival Models

chapter 8|7 pages

Competing Risks Models

chapter 9|12 pages

Causality and Matching