Survival analysis is used when working with data that may be censored, as often is the case in studies of human subjects with incomplete follow-up.  The presence of censoring makes most forms of regression and other standard statistical analyses inappropriate. A body of specialized techniques for analyzing this type of data has been developed, including methods for estimating and comparing marginal survival functions, and regression methods including the widely-utilized Cox proportional hazards model.  This workshop will briefly review the key principles of survival analysis, then illustrate by example how various survival analysis methods can be carried out using Python with the Statsmodels package.

Participants should bring a laptop if they want to work with the examples during the presentation, but this is optional.