Event History &
Survival Analysis

A five-day seminar taught by Paul D. Allison, Ph.D. 

Read 12 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

Event History & Survival Analysis covers both the theory and practice of survival methodology. Assuming no previous knowledge of survival analysis, this seminar 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.
  • How to handle left censoring.
  • 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.

computing

All examples and exercises will use SAS, but no previous experience with SAS is required. Four SAS procedures will be covered in detail: LIFETEST, LIFEREG, PHREG and LOGISTIC.  Lecture notes and exercises using Stata are available on request. 

This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments.  Additional time is available for exploring other sample data sets. Or you can bring your own data and try out new techniques as you learn them. To do the exercises, you will need to bring your own laptop computer with SAS (or Stata) installed. Power outlets will be provided at each seat. 


Who should attend?

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. 


Location, format, materials.

The seminar meets 9 a.m. to 5 p.m. on Monday through Friday at The Hub Commerce Square, 2001 Market Street, Philadelphia. 

Here is a typical day’s schedule:

9-12 Lecture
12-1 Lunch break
1-3 Lecture
3-5 Computing and consulting 

You’ll get a free copy of the Professor Allison’s book, Survival Analysis Using SAS® (second edition). You’ll also 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 notetaking.


Registration and lodging

The fee of $1695 includes all course materials. 

Lodging Reservation Instructions

A block of Standard rooms has been reserved at a special rate of $116 per night at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA. This hotel is an 8-minute walk to the course location. To make a reservation, call Club Quarters Member Services during business hours at 203-905-2100 and refer to the group code of STA714. To guarantee rate and availability, reservations must be made by June 15.  


Course outline

  1. Kaplan-Meier estimation
  2. Accelerated failure time models
  3. Types of censoring
  4. Maximum likelihood estimation
  5. Interpretation of parameters
  6. Proportional hazards models
  7. Partial likelihood estimation
  8. Competing risks
  9. Time dependent covariates
10. Discrete time analysis
11. Sensitivity analysis for censoring
12. Choice of time axis
13. Model choice and goodness of fit
14. Testing the proportional hazards assumption
15. Heterogeneity and time dependence
16. R-squared and standardized coefficients
17. Repeated events
18. Left censoring, left truncation


Comments from recent participants 

“This is an excellent class and gave a very good practical and applied introduction to survival analysis that you would not get if you took the class in a university setting. I learned a lot of useful techniques from Professor Paul Allison that can be applied to Marketing Analytics. The great thing about Professor Allison’s course is that it gives one a jump start in using this advanced method even if you don’t have a formal stats background. I already can apply it to Marketing Analytics using the SAS code he provided in the class.”
  Vijay Raghavan, Forest Labs

“This is the best applied statistics course I ever took. All concepts were explained in detail and very intuitively. It contained a large number of examples of how each method can be implemented in SAS.”
   Ioan Voicu, Financial Economist 

“Excellent course. I had read the book before but it is no substitute for the material presented in the course. A nice blend of lecture and practice that really helped solidify survival analysis for me and how it fits in my statistical toolkit.”
   Larry Hearld, University of Alabama 

“I’ve learned Survival Analysis from all kinds of sources. But this is the first time I ever took a complete and specific course on that and I felt like I’ve wasted all the time I spent before after the first day. The course is well paced and very informative while Paul can answer almost any question or concern that you have. I liked the structure of the course with some reviews for basic statistic concepts.”
   Siyu Zhou 

“This class was very useful. I am now more confident in my approach to handling data using proportional hazard modeling. Paul provides examples which are relevant and easy to understand. He is happy to answer questions and clarify difficult concepts. I look forward to taking his multiple imputation course.”
   Melissa Bagnell, Naval Health Research Center 

“All of the wisdom found in Dr. Paul Allison’s book on Survival Analysis is delivered in this intensive and succinct course. However, having the content listened to (rather than read), with directly relevant SAS based assignments, and the expert on hand to answer questions, naturally leads to a much greater understanding of the concepts than can possibly be delivered by a text.”
   Furqan Shaikh, The Hospital for Sick Children 

“With only a background in basic linear and logistic regression, I found the class remarkably valuable. Professor Allison’s ability to move from conceptual explanations to mathematical explanations to SAS application was incredibly easy to follow. I would recommend this course to anyone interested in looking at social phenomena in a new way.”
   Sonal Nalkur, Emory University

 “I have learned a lot during that course, I highly recommend it to anyone interested in learning more on survival analysis.”
   Adel AbouAli 

“Great job, Paul. Thank you for offering such a course.”
   Mourad Atlas, FDA 

“An invaluable experience for those steeped in a quantitative science.”
   Benjamin E. Moulton 

“Paul Allison’s Event History & Survival Analysis using SAS is a must have for statisticians and research professionally looking for an overview or refresher on how to utilize survival analysis techniques with medical or other time-to-event data. There was the right balance between theory and practical examples with the opportunity to ask questions and learn in a productive atmosphere.”
   Vivian B. Alderfer, ICON Clinical Research

“This is a great course to learn the utility of tine-to-event analysis using the various statistical methods available, especially in a short period of time.”
  Anonymous