An On-Demand Seminar Taught by
Paul D. Allison, Ph.D.
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.
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:
- Elementary Concepts of Survival Analysis
- Univariate Survival Analysis
- The Kaplan-Meier Method
- Cox Regression
- Time-dependent Covariates
- Competing Risks
- Discrete Time Analysis
- Some Tools and Tests
- Models for Non-proportional Hazards
- 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.
MORE DETAILS ABOUT THE COURSE CONTENT
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:
- 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?
- 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.
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.
“I attended the online Survival Analysis course offered by Statistical Horizons. The one aspect of the course that I am 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 like about the course offered online is that I can always rewind and listen again to some more complicated parts of a topic and make sure I can understand these parts. I think this is one big advantage of the same course offered online as opposed to being offered in a traditional classroom setting.”
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 from this course 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 knowledgeable epidemiologist as a result of this course. Lastly, another great thing about the course was being 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