Survival Analysis

An On-Demand Seminar Taught by
Paul D. Allison, Ph.D.

Read reviews 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. Now you can take that course in a convenient, on-demand format. 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. 

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.

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 that works in your web browser, called SAS OnDemand for Academics.

If you’d like to use R for this course but don’t yet have much experience with that package, here are some excellent on-line resources for building your R skills.

WHO SHOULD Register?

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.


“For me, this course stood out in terms of the depth of coverage of certain topics which are important but often omitted in introductory courses on survival analysis. Namely competing risks, discrete time analysis, and checking and managing the violation of proportional hazards assumption. Dr. Allison is very knowledgeable and willing to share his insights – I found the exercises and discussion forum very helpful in consolidating learning. The allocated 4-6 week duration of the on-demand format allows time to catch up on readings and reflect on the learning. Being a Stata user, I have learned new techniques which have enhanced my ability to carry out a more thorough and in-depth analysis.”
  Wee Shiong Lim, Tan Tock Seng Hospital

“The course is very informative and the concept was explained in a very clear and simple way. The examples and exercises given made me feel more confident to apply survival analysis in my future research. Thank you Dr. Allison for such a great course!”
  Reem Mulla, University of Waterloo

“I found the course to be very useful, and, in my opinion, a better way to learn the material than by in-person attendance. I learned a lot about survival analysis techniques. The online lectures allowed me to instantly review material I didn’t quite get the first time around. Professor Allison is a very good, clear presenter, and, offline, he always took time to respond to the written questions the students submitted.”
  Robert Gesumaria, Social Security Administration

“I attended the online Survival Analysis course offered by Statistical Horizons. The one aspect of the course that I was most impressed with is the knowledge and experience of Dr. Paul Allison, who taught this course. Additionally, his teaching style is such that he teaches every detail of all the topics calmly, gently, and clearly. I found his teaching easy to understand, even for some pretty complicated concepts. Another aspect I liked about the course offered online is that I could rewind and listen again to some more complicated parts of a topic and make sure I understood those parts. I think this is one big advantage of the same course offered online as opposed to being offered in a traditional classroom setting.”
  Dr. Eric Shiu, University of Birmingham

“As a result of this course on survival analysis, I feel more confident in conducting event history analyses. This course applied to my job position as an epidemiologist in more ways than I could have imagined, and I am already using the knowledge I gained as a part of the analyses I am conducting. Before this course, I never realized how much data I was “wasting” using my previous analysis methods. Thanks for this opportunity; I am a stronger, more confident, and more knowledgeable epidemiologist as a result. Lastly, another great thing about the course was that it was taught by the person, Dr. Allison, who has published so much on survival analysis.”
  Amanda Staudt, US Army MEDCOM AISR

“The survival analysis course is great! It answered many questions I had before taking this course. The examples are very helpful to understand the method. Highly recommend this course!”
  Mengting Li, Rutgers University