Survival Analysis

A 2-Day Seminar taught by Paul D. Allison, Ph.D. 

Read reviews of this course

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


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.  

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


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.


COMPUTING

This is a hands-on course with at least one hour each day devoted to carefully structured and supervised assignments.  To do the exercises, you will need to bring your own laptop computer with either SAS or Stata installed. Power outlets will be provided at each seat.

For Stata users, version 14 will be used for the examples, but the exercises can also be done with versions 12 or 13. 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.  


LOCATION, FORMAT AND MATERIALS

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103. 

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. 


Registration and lodging

The fee of $995.00 includes all seminar materials.

Refund Policy
If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $154 per night. This location is about a 5 minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code STA420 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, March 20, 2017. 

If you make reservations after the cut-off date ask for the Statistical Horizons room rate (do not use the code) and they will try to accommodate your request.


SEMINAR OUTLINE

  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 

RECENT COMMENTS FROM PARTICIPANTS

“Survival Analysis is presented at a level that beginners can understand but is comprehensive enough for experienced analysts to learn about a variety of topics unique to survival analysis, particularly those interested in working with time-varying covariates, competing risk models, and discrete-time survival models.”
  Andy Lin, University of California, Los Angeles

“This is a very nicely architectured course for beginners and intermediate level students. The supplied course material is self-explanatory with worked out examples – both in SAS and Stata. I found Professor Paul Allison to be a very experienced expert in subject matters and he has a brilliant technique to unfold complicated mathematical concepts in a simple way – that’s a game changer.”
  Kamal KC, State Farm Insurance Company

“Paul manages to fit a comprehensive overview of survival analysis into the two-day course, neither focusing too much on introductory materials or more advanced theory. There is also good balance of discussion of intuition for methods (for the less technically inclined) and theory (e.g., the separability of likelihood functions in PH models). I did not know much about survival analysis, but could code KM analysis in Excel at the end, and had good comfort with Cox/PH modeling in Stata. Overall, I took away immediately applicable information from the course.”
  Tom O’Connell, Medicus Economics, LLC

“I found this course to be a great mix of both theory and practical application. I would definitely recommend this course!”
  Joanna Bryan, Weill Cornell Medicine College 

“Paul’s 2-day Survival Analysis class provided a very thorough introduction to the topic. I feel I came away from the class with concrete skills that I could readily apply.”
  Matthew Jones, Walden University

“I really liked the practical approach with exercises and demonstrations of command. The examples and solutions together with my notes will be helpful to review even later in my career when facing new datasets and new questions than those I have right now.”
  Anne-Karine Melsom, The University of Michigan/The University Hospital of   Northern Norway

“This course defines FANTASTIC! I’m so impressed by all of what we were able to cover in just two days. The value of the course is, well, invaluable. I will definitely be signing up for other courses and telling everyone about Statistical Horizons!”
  Valerie Stackman, University of Wisconsin-Platteville

“Survival Analysis with Paul helped me grasp the topic in great detail capturing both the theoretical and practical aspects of the course. I thoroughly enjoyed the seminar. Paul clearly articulates the motivation assumption, code and interpretation of proportional hazard models. Given Paul’s familiarity and experience, I really appreciated his insightful observation around which methods are appropriate under specific scenarios.”
  Rahul Sharma, Capital One