April 2016 Member Webinar: An Introduction to Kaplan-Meier Curves

by guest

Survival data models provide interpretation of data representing the time until an event occurs. In many situations, the event is death, but it can also represent the time to other bad events such as cancer relapse or failure of a medical device. It can also be used to denote time to positive events such as pregnancy. Often patients are lost to follow-up prior to death, but you can still use the information about them while they were in your study to better estimate the survival probability over time.

This is done using the Kaplan-Meier curve, an approach developed by Edward Kaplan and Paul Meier in 1958.

In this member, you will see a simple example of this using fruit fly data, and learn how to interpret the Kaplan-Meier curve to estimate survival probabilities and survival percentiles.

Note: This webinar is an exclusive benefit for members of the Statistically Speaking Membership Program.

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. He has previously worked at Children’s Mercy Hospital, the National Institute for Occupational Safety and Health, and Bowling Green State University.

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, www.pmean.com. 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.

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You’ll get exclusive access to this month’s webinar live, weekly live Q&A sessions, a private stats forum, 60+ recordings of past webinars (including this one), and more.

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