Survival Analysis

An Online Seminar Taught by
Paul D. Allison, Ph.D.

Read reviews of the in-person version of this seminar

To see a sample of the course slides, click here.

For the last 25 years, Dr. Paul Allison has been teaching his acclaimed two-day seminar on Survival Analysis to audiences around the world. This seminar covers both the theory and practice of statistical methods for event-time data. Participants receive a thorough introduction to such topics as censoring, Kaplan-Meier estimation, Cox regression, discrete-time methods, competing risks, and repeated events. 

Starting April 3, we will be offering this seminar online for the first time. The course takes place in a series of four weekly installments of videos, quizzes, readings, and assignments, and requires about 6-8 hours/week. You can participate at your own convenience; there are no set times when you are required to be online. The course can be accessed with any recent web browser on almost any platform, including iPhone, iPad, and Android devices. It consists of 10 modules:

  1. Elementary Concepts of Survival Analysis
  2. Univariate Survival Analysis
  3. The Kaplan-Meier Method
  4. Cox Regression
  5. Time-dependent Covariates
  6. Competing Risks
  7. Discrete Time Analysis
  8. Some Tools and Tests
  9. Models for Non-proportional Hazards
  10. Repeated Events

The modules contain videos of the live, 2-day version of the course in its entirety. Each module is followed by a short multiple-choice quiz to test your knowledge. There are also weekly exercises that ask you to apply what you’ve learned to a real data set. You may submit your work for review by Dr. Allison.  

Each week, there are 2-3 assigned articles to read. There is also an online discussion forum where you can post questions or comments about any aspect of the course. All questions will be promptly answered by Dr. Allison. 

Downloadable course materials include the following pdf files:

  • All slides displayed in the videos.
  • Exercises for each week. 
  • Readings for each week.
  • Computer code for all exercises (in SAS, Stata, and R formats).
  • A certificate of completion.


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, marketing, and many other fields. 

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.

This is a hands-on course with ample opportunity for participants to practice survival analysis.

The examples and exercises will emphasize SAS and Stata, but slides and code will also be provided for R.


To do the exercises, you will need a computer with Stata, SAS, or R installed.

For Stata users, version 16 will be used for the examples, but the exercises can also be done with versions 13-15. Seminar participants who are not yet ready to purchase Stata could take advantage of StataCorp’s free 30-day evaluation offer or their 30-day software return policy.

There is now a free version of SAS, called the SAS University Edition, that is available to anyone. It has everything needed to run the exercises in this course, and it will run on Windows, Mac, or Linux computers. However, you do need a 64-bit machine with at least 1 GB of RAM. You also have to download and install virtualization software that is available free from third-party vendors. The SAS Studio interface runs in your browser, but you do not have to be connected to the Internet. The download and installation are a bit complicated, but well worth the time and effort.

If you’d like to use R for the course but are concerned that your R skills aren’t sufficient, there are excellent on-line resources for learning the basics. Here are our recommendations.


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.

reviews of the live version of Survival Analysis

“This course was great both as a refresher of basic survival analysis methods and an introduction to more complex survival analysis methods. I really enjoyed the opportunities to work with real data. Thank you for such a useful class – I now feel much more comfortable with implementing these methods in my work!”
  Si-Tien Wang, Medicus Economics LLC

“I liked the openness of Professor Paul. He is open to any questions you may have. The course is inclined to applied sections with some important theoretical discussions. Thanks for your long-term service to the academic.”
  Murshed Chowdhury, University of New Brunswick

“I really enjoyed Dr. Allison’s Survival Analysis course. I’ve been working in the oncology field for about 15 years. Survival Analysis is a crucial piece in our field. Dr. Allison’s class introduced the concept very clearly. The course included various topics that are commonly applied to primary and exploratory analyses in oncology research. I highly recommend this class for people who need in-depth knowledge of survival analysis.”
  Yihua Lee, Pharmacyclics LLC

“Thank you very much, Dr. Allison, for your wonderful lectures. I truly enjoyed it and have learned a lot from this course. This training (topics) has not been covered in my previous trainings. Now I have obtained more skillsets to complete my own research and gain more confidence about my analysis. They’re truly practical and useful in my work.”
  Lucy Xu, Harvard Medical School

“I feel like I can confidently go into a survival analysis project using materials from this course thanks to Paul’s practical examples, slides, and references specifically.”
  Scott Oglesbee, The University of New Mexico

“The course is extremely useful in setting up and interpreting standard survival techniques (KM Cox Regression, Simple Time-Dependent Covariate Analysis), since these are the techniques used in most situations.”
  Sandhya Upasani, Pharmacyclics LLC

“I was concerned that my somewhat limited knowledge of Stata would interfere with learning this method, but the entire workshop was delivered in a very digestible format. The variety of examples used throughout the course to demonstrate the application of survival analysis was beneficial. I am confident I can conduct analysis using this method to better answer pressing research questions. Thank you!”
  Raven Weaver, Washington State University

“Great class as always. I’ve benefited a lot from several classes offered by Statistical Horizons.”
  Haowei Wang, University of Massachusetts, Boston

“The Survival Analysis course provided me with a broad foundational working knowledge for this collection of methods.”
  Nathan O’Hara, University of Maryland

“While I had gone through a survival analysis course previously, Dr. Allison was able to simplify the terminology and mechanics of the methods. I highly recommend this course as someone new to the method or as a refresher and clear presentation.”
  Apri Medina, University of California, Santa Cruz