发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生存数据删失导致的信息丢失，提出利用治疗后疾病史的目标保持估计器，通过影响函数和增益恒等式提升因果效应估计效率，并验证于乳腺癌数据。
AI 中文摘要
在具有生存终点的临床试验中，当参与者在观察到死亡之前被删失时，信息会丢失。我们开发了目标保持估计器，利用治疗后疾病史（如复发或进展）来恢复因删失而丢失的信息，以估计边际生存效应和受限平均生存效应。难点在于中间事件位于治疗的下游：朴素调整可能改变因果估计量，而有用信息仅通过观察到的粗化过程进入。我们推导了有无复发史情况下的观测数据影响函数，并获得了精确的增益恒等式。该恒等式表明，效率提升由删失风险、存活风险集在复发状态间的划分以及这些状态间残余生存的分离程度驱动。在无协变量的疾病-死亡模型中，Aalen-Johansen估计器在标准化到边际目标后实现了复发增强的有效得分。在有协变量的情况下，正确设定的Cox-Breslow转移风险提供了根号n插件基准，而风险诱导的一步估计器则提供了速率稳健性，并在原始转移和删失学习器速率下，通过二阶乘积余项实现典范推断。一项基于数字化无进展生存期和总生存期曲线校准的半合成转移性乳腺癌研究说明了增益恒等式。该框架广泛适用于具有信息性中间历史的删失时间-事件研究。
英文摘要
Clinical trials with survival endpoints lose information when participants are censored before death is observed. We develop target-preserving estimators that use posttreatment disease history, such as recurrence or progression, to recover information lost to censoring for marginal survival and restricted mean survival effects. The difficulty is that the intermediate event is downstream of treatment: naive adjustment can change the causal estimand, and the useful information enters only through the observed coarsening. We derive observed-data influence functions with and without recurrence history and obtain an exact gain identity. The identity shows that efficiency improvement is driven by the censoring hazard, the split of the alive risk set into recurrence states, and the residual-survival separation between those states. In the no-covariate illness-death model, the Aalen-Johansen estimator realizes the recurrence-augmented efficient score after standardization to the marginal target. With covariates, correctly specified Cox-Breslow transition hazards provide a root-N plug-in benchmark, while a hazard-induced one-step estimator gives rate robustness and, under primitive transition and censoring learner rates, canonical inference with a second-order product remainder. A semi-synthetic metastatic breast cancer study calibrated from digitized progression-free survival and overall survival curves illustrates the gain identity. The framework applies broadly to censored time-to-event studies with informative intermediate histories.
Comments90 pages, 9 figures
