Rforce: Random Forest for Composite Endpoints

Oregon Convention Center 

Medical research often involves the study of composite endpoints that combine multiple clinical events to assess the efficacy of treatments. When constructing composite endpoints, it is a common practice to analyze the time to the first event. However, this approach overlooks outcomes that occur after the first event, resulting in information loss. Furthermore, the terminal event can not only be of interest but also a competing risk for other types of outcomes. While regression models exist to analyze all types of such outcomes, not just the first event, and properly address the terminal event, they do not account for nonlinear relationships between covariates and composite endpoints. To address these important issues, we introduce Random FORest for Composite Endpoints (Rforce) consisting of non-fatal composite events and terminal events. The proposed method handles the dependent censoring due to the terminal events with the concept of pseudo-risk time. In simulation studies, Rforce demonstrates comparable performance with existing regression-based models under linear settings and outperforms competing methods under non-linear settings.