Semiparametric Estimation of Delayed-Outcome Treatment Effects Using Short-Term Surrogates under Administrative Censoring
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行政删失下利用短期替代指标半参数估计延迟结果处理效应
- 研究问题
- 在长期结果因日历设计被行政删失、部分观察概率很低时，如何利用所有单位均可观察的短期替代指标估计平均处理效应？
- 方法与思路
- 作者以嵌套积分表达识别目标，分析密度代入估计的二阶余项，再提出通过处理加权整合经验替代指标分布的辅助增广逆概率估计。
- 材料陈述的贡献
- 摘要称所提估计器从结构上避免特定密度估计交叉余项，并在乘积速率双重稳健条件下获得聚类数平方根速率及渐近线性。
- 阅读关注点
- 应核对处理效应识别、替代指标与删失的条件独立及支持条件，检查避免该余项为何不等于消除全部低观察概率问题。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实。摘要未列完整识别和乘积速率条件，也未说明观察概率接近零时方差如何表现。
原文摘要
The multi-site registry studies, such as Stepped-wedge cluster-randomized trials (SW-CRT), staggered-enrollment RCTs, etc., share a structural feature: the primary long-term outcome is administratively censored for a non-negligible fraction of units, with censoring driven by calendar design rather than by the outcome itself. Standard inverse-probability-of-censoring weighting becomes unstable when observation probabilities $g_Δ$ concentrate near zero for late-crossing units, while parametric mixed-model analyses discard the information in any short-term intermediate measurement and rely on correct specification of the secular time trend. We study semiparametric estimation of the average treatment effect when a short-term surrogate, which is observed for all units and conditionally independent of the censoring mechanism given baseline covariates, is available. Identification takes a nested-integral form in which the outcome regression is marginalized over the conditional surrogate distribution, so the observation mechanism does not enter the target functional as an inverse weight. We show that a density-plug-in one-step debiased machine-learning construction for this functional leaves a second-order cross-product remainder $R_{SY}$ that has no doubly-robust complement in the efficient influence function and is not eliminated by cross-fitting . We propose a surrogate-assisted AIPW estimator (SA-AIPW) that integrates over the empirical surrogate distribution through treatment weighting rather than estimating the conditional surrogate density, and so structurally avoids $R_{SY}$. For clustered data, the estimator is shown to be $\sqrt{J}$-consistent and asymptotically linear under a product-rate double-robustness condition.