Introduction to Survival Analysis

Survival Analysis sounds like it should have a very narrow focus, but in fact, it's an incredibly useful set of statistic tools that apply well in many fields. 

The defining feature of these models is that the dependent variable is the time until an event occurs. They were originally developed for mortality events--understanding the relationship between predictors and how long after a treatment patients survive.

But it turns out you can adapt them to many other time-to-event outcomes, such as customer churn, restaurant closings, successful completion of training for guide dogs, length of unemployment, defoliation of plants, and metal fatigue. The outcomes do not necessarily have to be “bad” events or anything to do with surviving.

Join Steve Simon as he introduces you to some fundamental tools and concepts within survival analysis.

Covered in this webinar: 


See and learn how to interpret a variety of Kaplan-Meier curves, the fundamental graphical display for survival data


The underlying calculations of a Kaplan-Meier curve


An advanced application of competing risks analysis using a Political Science example of duration of leadership in the world's countries

About the Instructor
Steve Simon works as an independent statistical consultant and as a part-time faculty member in the Department of Biomedical and Health Informatics at the University of Missouri-Kansas City.

Steve has over 90 peer-reviewed publications, four of which have won major awards. He has written one book, Statistical Evidence in Medical Trials, and is the author of a major website about Statistics, Research Design, and Evidence Based Medicine,

​​​​​​​One of his current areas of interest is using Bayesian models to forecast patient accrual in clinical trials. Steve received a Ph.D. in Statistics from the University of Iowa in 1982.