Estimand and Causal Framework for Cardiovascular Trials with Non-Susceptible Fractions

We additionally evaluate robustness to non-proportional hazards among susceptibles and quantify bias in both incidence and latency estimands across identifiability conditions. Non-susceptibility-aware estimands recover distinct causal mechanisms with superior interpretability. Application to two large cardiovascular outcome trials suggests that, to the extent an effect is present, it is concentrated in the incidence component—an increase in the probability of long-term event-free survival—with little evidence of an effect on event timing among susceptible individuals; however, both components are estimated imprecisely under the observed follow-up, so this decomposition is indicative rather than definitive.

This distinction is entirely obscured by conventional hazard ratios. The proposed framework—with explicit formal identification conditions, explicitly layered by causal role and testability, clarified estimand targets, and an open-source interactive dashboard and Statistical Analysis Plan template—equips statisticians and regulators with practical tools to define and interpret treatment effects when sustained event-free survival is biologically plausible.</p>