B1150
Title: Causal inference with competing events
Authors: Jessica Young - Harvard Medical School and Harvard Pilgrim Health Care Institute (United States) [presenting]
Abstract: A competing (risk) event is any event that makes it impossible for the event of interest in a study to occur. For example, cardiovascular disease death is a competing event for prostate cancer death because an individual cannot die of prostate cancer once he has died of cardiovascular disease. Various statistical estimands have been posed in the classical competing risks literature, most prominently the cause-specific cumulative incidence, the marginal cumulative incidence, the cause-specific hazard, and the subdistribution hazard. The interpretation of counterfactual contrasts is discussed in each of the estimands under different treatments and possible limitations in their interpretation are considered when a causal treatment effect on the event of interest is the goal and treatment may affect future event processes. In turn, choosing a target causal effect in the setting is argued to fundamentally boil down to whether or not estimating total effects achieves satisfaction, capturing all mechanisms by which treatment affects the event of interest, including via effects on competing events. When the total effect is deemed insufficient to answer the underlying question, alternative targets of inference are considered that capture treatment mechanisms for competing event settings, with emphasis on the recently proposed separable effects.
