The student is able to 1) identify survival data 2) generate appropriate summary statistics and graphical representations and interpret them 3) apply and interpret log-rank tests and Cox proportional hazards models.

This course introduces the essential methods for modeling and interpreting survival data or, more generally, time-to-event data. Survival models are widely used in clinical, epidemiological and a variety of health related fields. General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates. Special topics of multistate models and competing risks will also be briefly considered.

Basic knowledge of biostatistics, regression models and statistical software (R) is necessary to succeed on the course. If you have no prior experience with R, one option to fulfill that gap is to take the CAST online introductory course (https://www.uta.fi/cast/_admin/index.php/events/Ronline.html).