Survival Analysis Using Stata
A 2-Day Seminar on the Analysis of Event Data
Taught by Paul D. Allison, Ph.D.
Read 5 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.
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:
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.
The seminar meets on Friday, April 13, and Saturday, April 14, at the Courtyard Washington Embassy Row, located at 1600 Rhode Island Avenue, NW, Washington,DC. Class will begin at 9 a.m. each day and end at approximately 4 p.m., with a 1-hour break for lunch. A block of guest rooms has been reserved at the hotel (see Lodging Tab at the top of this page for details).
fee of $795.00 includes all seminar materials.
All 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.
All examples and lecture notes will use Stata. However, lecture notes using SAS are available on request.
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
15. Heterogeneity and time dependence
17. Repeated events
18. Left censoring, left truncation
Of the 18 participants who completed course evaluation forms, 12 rated it "excellent" and 6 rated it "very good."
“I greatly enjoyed this course and thought it was very comprehensive and covered most of the important issues in conducting survival analysis. I highly recommend it to any scientist working with time to event data.”
May Baydoun, National Institute on Aging
“As someone who uses statistics in a strictly applied (non-academic) setting, I am always a bit intimidated by courses such as this. However, Dr. Allison’s presentation is crystal-clear, allowing students to understand both the theoretical underpinnings and the potential applications of survival analysis.”
Sarah Hurley, Youth Villages
“This is an excellent refresher course on survival analysis that brings the student up to date. Both continuous time and discrete time methods are covered in detail, and nuances of applying each type of method are discussed well. An excellent investment of two days’ time.”
“This is a very structured and informative class. Very applicable for applied researchers.”
“The courses provides detailed explanations with proper real empirical examples, makes it easier to understand and to apply in research. The instructor makes complicated material easy to understand. The lecture notes will be valuable resources for later work.”
Yuping Zhang, Lehigh University
The course fee is $795, which includes all course materials. All major credit cards are accepted via online registration.
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