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 this sensitivity by using only information measured prior to the intercurrent event. Our key idea is to compare treated and untreated individuals, matched on baseline covariates, at the most recent time point before either experiences an intercurrent event. We call these contrasts Pairwise Last Observation Time (PLOT) estimands. PLOT estimands are identified in randomized trials without structural assumptions, even under severe positivity violations. Although PLOT-based tests may theoretically be susceptible to residual selection bias, we show this bias vanishes under standard conditions and remains negligible in extensive simulations. We develop asymptotically efficient, model-free tests and treatment effect estimators using data-adaptive nuisance parameter estimation. We evaluate performance via simulation and apply the method to re-analyze the DEVOTE trial, affected by truncation by death. PLOT offers a robust, data-driven alternative for evaluating treatment efficacy in the presence of complex intercurrent events.

翻译：在随机临床试验中，治疗切换、救援用药、脱落或死亡截断等中间事件常使意向性治疗分析复杂化。现有因果推断框架通常针对假设性或主层估计量（如幸存者平均因果效应），这些方法依赖于不可验证的假设，且对未测量的混杂因素或正性假设违例较为敏感。我们提出一种新方法，通过仅使用中间事件发生前测量的信息来降低这种敏感性。我们的核心思想是在基线协变量匹配的条件下，比较治疗组与未治疗组个体在任一方发生中间事件前最近时间点的观测结果。我们将这些对比称为成对末次观测时间估计量。PLOT估计量可在随机试验中无需结构假设即可识别，即使在严重正性违例下仍成立。尽管基于PLOT的检验理论上可能受残余选择偏倚影响，但我们证明该偏倚在标准条件下可忽略，且在大量模拟中保持可忽略水平。我们利用数据自适应型冗余参数估计，开发了渐近有效的无模型检验与治疗效果估计量。通过模拟评估性能，并将该方法应用于受死亡截断影响的DEVOTE试验再分析。PLOT为存在复杂中间事件时的治疗效能评估提供了稳健的数据驱动替代方案。