STATISTICS Seminars Series: Elisavet Syriopoulou

Topic Evaluating mediator interventions for time-to-event outcomes: A causal framework for cancer disparities

Abstract Understanding cancer survival disparities often requires evaluating the impact of potential interventions on mediators that lie between an exposure and an outcome. Causal mediation analysis can be applied in such settings, and it allows exploring such interventions in a systematic way. While traditional approaches have focused on uncovering mechanistic pathways, despite the presence of ill-defined interventions, recent developments have introduced interventional effects that map target trials and focus on evaluating shifts in mediator distributions. I will present some ongoing work in which we extend these interventional effects to settings with time-to-event outcomes and incorporate a relative survival framework. Relative survival is a commonly used measure in cancer epidemiology used to estimate net survival, i.e. the disease-specific survival, without needing to utilise the cause of death information obtained from cancer registers that may be inaccurate or not available. An advantage of incorporating the relative survival framework in mediation analysis is that it allows evaluating the impact of interventions on all-cause survival differences after targeting cancer-specific differences. This is particularly important as it isolates cancer-related differences, and it may be easier to study compared to looking at both cancer-related and other pathways. For example, through the relative survival framework is possible to estimate the potential gains in survival for those with the worst prognosis if we could eliminate stage (or treatment) differences between socioeconomic groups and improve cancer-specific survival while keeping background survival differences constant.