Biostatistics Journal Club: Modeling and Joint Prediction of Semi-Competing Risks
Semi-competing risks refers to the survival analysis setting where the occurrence of a non-terminal event is subject to whether a terminal event has occurred, but not vice versa. Semi-competing risks arise across a broad range of clinical contexts, but are not always recognized as such, leading researchers to pursue analyses that ignore the dependence between events, or focus solely on a single or composite outcome. In particular, unlike standard competing risks, semi-competing risks provide an opportunity to learn about the joint risk of the two events, enabling individualized risk prediction of patients’ entire outcome trajectories across time.
In this talk, Harrison Reeder, PhD, of Massachusetts General Hospital, will discuss familiar survival analysis tools and introduce the frailty-based illness-death model for semi-competing risks. This framework captures the complex interplay between risk factors and the joint outcomes, and aligns with the needs of clinical decision makers. He will additionally illustrate these dynamics with recent work on joint prediction in two clinical settings: preeclampsia and delivery during pregnancy; and, device activation and death among heart failure patients receiving implantable cardioverter defibrillators.