Survival Analysis with STATA

The aim of the course is to familiarize the participant to the techniques of survival analysis with practical applications with a variety of datasets. Practical applications in Stata are offered that give participants hands-on experience to datasets and the econometric techniques covered in the course. By the end of the course students will be able to distinguish between survival analysis and ordinary regression designs, conduct survival analysis and interpret results, compare between survival analysis models and assumptions, and perform forecasting in survival analysis contexts.

  • Session 1: Introduction to survival analysis / Comparison to regression designs / Data organization
  • Session 2: Non-parametric survival analysis models
  • Session 3: Semi-parametric survival models / Cox Proportional Hazards model
  • Session 4: Parametric survival models
  • Session 5: Accelerated Failure Time (AFT) models / Discrete-time models
  • Session 6: Competing risks / Repeated events modeling
  • Session 7: Forecasting with survival analysis models
  • Session 8: Practical sessions: Data organization / Non-parametric models
  • Session 9: Practical sessions: Semi-parametric models
  • Session 10: Practical sessions: Parametric models
  • Session 11: Practical sessions: AFT models / Competing risks
  • Session 12: Practical sessions: Forecasting


The course is designed for participants (MSc/PhD students, researchers, academics, analysts) with some familiarity in banking, finance, and econometrics. No experience with survival analysis is necessary.


A series of lectures will deliver the material using slides presentation. A reading list of academic papers will be provided to the participants before the start of the course. A series of textbooks and chapters will be recommended for additional reading. Practical sessions require the use of Stata econometric software. A brief introduction to Stata for less familiar students may be arranged separately.

To register, please visit the course website.