Robust evaluation of treatment effects in longitudinal studies with truncation by death or other intercurrent events
死亡截断及其他中间事件下纵向研究治疗效应的稳健评估
Intercurrent events, such as treatment switching, rescue medication, dropout, or truncation by death, frequently complicate intention-to-treat analyses in randomized clinical trials. Existing causal inference frameworks typically target hypothetical or principal stratum estimands (e.g., survivor average causal effects), which rely on unverifiable assumptions and can be sensitive to unmeasured confounders or positivity violations. We propose a novel approach that mitigates…