2016 Stata Summer School:

Survival Analysis Using Stata

Taught by Paul Allison, Ph.D.
August 17-18, Hotel Birger Jarl Conference
Stockholm, Sweden 

Read reviews of this seminar 

For event-time data, ordinary regression analysis won’t do the job

If you’ve ever used regression analysis on longitudinal event data, you’ve probably come up against two intractable problems:

1. Censoring: Nearly every sample contains some cases that do not experience an event. If the dependent variable is the time of the event, what do you do with these “censored” cases?

2. Time-dependent covariates: Many explanatory variables (like income or blood pressure) change in value over time. How do you put such variables in a regression analysis? 

Makeshift solutions to these questions can lead to severe biases. Survival methods are explicitly designed to deal with censoring and time-dependent covariates in a statistically correct way. Originally developed by biostatisticians, these methods have become popular in sociology, demography, psychology, economics, political science, and marketing.

How you will benefit from this seminar

Survival Analysis covers both the theory and practice of survival methodology. Assuming no previous knowledge of survival analysis, this course will turn you into a knowledgeable and skilled user of these indispensable techniques. Here are a few of the skills you will acquire:

  • How to organize survival data.
  • How to choose the right time axis.
  • When to use discrete vs. continuous time methods.
  • What to do about nonproportionality.
  • How to compute R-squared.
  • When and how to correct for unobserved heterogeneity.
  • How frequently to measure independent variables.
  • What to do if there is more than one kind of event.
  • How to test for sensitivity to informative censoring.  


If you need to analyze longitudinal event data and have a basic statistical background, this seminar is for you. You should have a good working knowledge of the principles and practice of multiple regression, as well as elementary statistical inference. But you do not need to know matrix algebra, calculus, or likelihood theory. 

Previous participants have come from a wide variety of fields: sociology, demography, psychology, economics, management, finance, history, marketing, biology, medicine, veterinary medicine and criminal justice.


This seminar will use Stata 14 for the many empirical examples and exercises. However, no previous experience with Stata is assumed. Lecture notes and exercises using SAS are also available on request. To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer.  If you do not already have Stata installed, a temporary license will be provided free of change. A power outlet and wireless access will be available at each seat.


Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking. 

All examples and lecture notes will use Stata. However, lecture notes using SAS and R are also available on request. 

The following Stata commands will be covered:  stset, streset, sts list, sts graph, sts test, stcox, stcurve, stcrreg, stcompet, logistic, mlogit, cloglog, reshape.


 Please go to the Metrika website for information on registration, and discounted hotel accommodations.


1.  Fundamentals of survival analysis
2.  Problems with conventional methods
3. Types of censoring 
4. Kaplan-Meier estimation 
5. Proportional hazards models 
6. Partial likelihood estimation 
7. Interpretation of parameters 
8. Competing risks 
9. Time dependent covariates 
10. Discrete time analysis 
11. Sensitivity analysis for censoring 
12. Choice of time axis 
13. Testing the proportional hazards assumption 
14. Stratification
15. Heterogeneity and time dependence 
16. Repeated events 
17. Left censoring, left truncation


“This course was a joy to take. It was superbly well organized and presented with great clarity.  I would recommend this course to anyone like myself who is not intimately familiar with survival analysis but thinks it might profitably be applied to their field of study. Thanks for a great presentation.”
  Steven Younkin, Mayo Clinic 

“I was apprehensive coming in to this course about whether my inchoate understanding of Survival Analysis would make the material covered difficult to follow – this was not true. Paul does an exceptional job explaining the theoretical basis for these methods and walks you through the steps to perform these analyses using Stata. Amazing how much you can learn in two days!”
  Brien Goodwin, UMass 

“While I publish at least one academic article per year, I am a practitioner (i.e. Senior Business Analyst). This course is well documented and inclusive in that if your company demands that you use a different program, like R, this class gives you instructions on how to use multiple platforms. This is the MAIN reason I took this class. I drove for hours for this and I am excited to take what I have learned and use it at work.”
  Jonathan Brackens, Borets 

“The practice and experience I gained in the Survival Analysis Using Stata course will help expedite the completion of my PhD thesis and will position me to take on new and challenging research projects in the future.”
  Isabelle Vallerand, University of Calgary 

“The course was perfect for active researchers: compact, efficient, thorough, and highly applicable to my own project ideas. Thank you Paul, looking forward to future training.”
  Matt Nobles, Sam Houston State University 

“This course provides a very efficient way to better understand survival analysis and it is likely to save you a lot of time compared to studying it yourself. Overall, it’s very helpful.”
  Lei Zhang, University of South Florida  

“Course material presented very well and easily understood.”
  Carlos Rocha, United States Marshals Service 

“I would recommend this course to anyone needing a basic understanding of survival analysis.”
  Mary Abraham, University of Kansas 

“Practical, useful, easy to understand! You’ll be equipped with important knowledge about survival analysis after you take the course. Dr. Paul Allison is an exceptional instructor to convey complex statistical concepts in plain language.”
  Ling Na