A0885
Title: Causal inference for time-to-event data with a cured subpopulation
Authors: Yi Wang - Shanghai University of International Business and Economics (China) [presenting]
Abstract: When studying the treatment effect on time-to-event outcomes, it is common that some individuals never experience failure events, which suggests that they have been cured. However, the cure status may not be observed due to censoring, which makes it challenging to define treatment effects. Current methods mainly focus on estimating model parameters in various cure models, ultimately leading to a lack of causal interpretations. To address this issue, two causal estimands are proposed, the timewise risk difference and mean survival time difference, in the always-uncured based on principal stratification as a complement to the treatment effect on cure rates. These estimands allow the study of the treatment effects on failure times in the always-uncured subpopulation. The identifiability is shown using a substitutional variable for the potential cure status under the ignorable treatment assignment mechanism; these two estimands are identifiable. Estimation methods are also provided using mixture cure models. The approach is applied to an observational study that compared the leukemia-free survival rates of different transplantation types to cure acute lymphoblastic leukemia. The proposed approach yielded insightful results that can be used to inform future treatment decisions.
