Evelyn Brodeur
Research Mentor: Nicholas Hartman
Mentor Department: Biostatistics, Public Health
Author(s): Evelyn Brodeur, Nicholas Hartman
Session: Session 1 (9:00 AM – 9:50 AM)
Presentation Type: Poster 76
Abstract
Cumulative gain metrics have long been used as measures of performance for classification models with binary health outcomes, describing the ability to identify high-risk groups from broader patient populations. Here, we develop novel extensions of cumulative gain metrics for time-to-event data structures to evaluate the efficacy of survival models in predicting the mortality risk of organ transplant candidates. Risk scores such as the Model for End-Stage Liver Disease (MELD) and Estimated Post-Transplant Survival score (EPTS) are utilized in clinical settings to decide the priority of organ recipients and predict long-term survival of patients with chronic disease. The validation of these models is essential for ensuring accurate treatments, reducing deaths on transplant waitlists, and reliably estimating patient outcomes. To assess these survival scores, we develop three versions of cumulative gains for survival data. The first method binarizes the survival outcome at a selected timepoint. Our second approach derives a new cumulative gains formula in terms of estimated survival functions. The third method uses a sequence of longitudinal binary outcomes to assess time-dependent risk scores. These methods vary in their handling of censored data, response to changes in risk, and ability to discriminate events from non-events. We apply each approach to national liver transplant registry data and calculate the proportion of total possible gains captured to analyze the performance of recently updated MELD scores and transplant policy.
