We often encounter data involving start and end times – human lifetimes, disease progression, employment status, or mechanical equipment failure, for example. The analysis of these time-to-event data involves some interesting techniques, such as hazard functions, censoring, and competing risks. We will introduce and use the most common approaches for analyzing these data, including Kaplan-Meier analysis and Cox regression. Lastly, we briefly discuss more complicated approaches like survival trees and bootstrapping. Attendees should leave with an understanding of a range of techniques they can use for their data. Although this is not a coding workshop, some R packages will be suggested.
Prerequisite: Participants should be familiar with introductory-level statistics, including the concepts of significance tests, confidence intervals, and p-values.