Survival analysis is the branch of statistics that deals with analyzing data when the outcome of interest is the time to some event, such as time to death or disease progression. Such outcomes are often only partially observed due to participants dropping out of the study or not having experienced the event of interest before the end of the study period (referred to as censoring). This partial missingness creates statistical challenges and several faculty members work on developing methods to address these challenges. Dr. Steingrimsson works on adapting machine learning algorithms for censored data and Dr. Chrysanthopoulou works on approaches for simulating time to event  (accounting for censoring) data in the context of complex simulation models (e.g., microsimulation models) used in Public Health Decision Making. In addition, several faculty members are involved in interdisciplinary collaborations that involve analysis of time-to-event outcomes.