随时间刻画幸存者主分层：识别、高效估计与敏感性分析
Characterizing Survivor Principal Strata Over Time: Identification, Efficient Estimation, and Sensitivity Analysis
- Johns Hopkins Bloomberg School of Public Health（约翰霍普金斯大学彭博公共卫生学院）
- Massachusetts General Hospital（麻省总医院）
- University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对纵向研究中幸存者层随时间变化的问题，提出用标准化均值差和KL散度刻画幸存者与基线人群的协变量差异，给出高效估计与敏感性分析，提升幸存者分析的可解释性。
中文摘要 AI 辅助
在纵向研究中，当功能结局受截断性死亡影响时，常报告按时间索引的幸存者平均因果效应估计值，该估计值定义于无论治疗分配如何均能存活的个体中。由于幸存者层随时间变化，所得效应估计可能对应于具有不同特征的人群。我们提议在幸存者平均因果效应估计的同时，附带幸存者与基线人群之间协变量差异的度量。我们分别研究标准化均值差（SMD）和Kullback-Leibler（KL）散度作为基于矩和基于分布的汇总。我们证明，在标准主分层假设的一个子集下，这些差异度量是可识别的，且其识别涉及幸存者分析中已估计的干扰函数。我们推导出有效影响函数，从而能够构造具有良好大样本性质的一步偏差校正估计量，并允许使用机器学习进行干扰估计。我们证明，在随机化研究中，SMD的一步估计对任意干扰模型误设具有稳健性，而KL散度和更广泛的密度比估计量对干扰误设更为敏感。此外，我们提供了一个敏感性分析框架，部分放宽了识别所需的单调性假设，并为所得界限构造了一步估计量。通过模拟研究，我们展示了所提估计量的有限样本性能，并说明差异度量如何提高幸存者分析的可解释性。最后，我们使用来自共享资源开放获取肌萎缩侧索硬化临床试验数据库的数据，评估肌萎缩侧索硬化（ALS）试验中幸存者层随时间变化的协变量差异。
英文摘要
Longitudinal studies with functional outcomes subject to truncation-by-death frequently report time-indexed survivor average causal effect estimates, defined among individuals who would survive regardless of treatment assignment. Because the survivor stratum can change over time, the resulting effect estimates may pertain to populations with different characteristics. We propose accompanying survivor average causal effect estimates with measures of covariate divergence between the survivor and baseline populations. We study standardized mean differences (SMDs) and the Kullback-Leibler (KL) divergence as moment-based and distributional summaries, respectively. We show that these divergence measures are identified under a subset of standard principal stratification assumptions and that their identification involves nuisance functions already estimated in survivor analyses. We derive the efficient influence functions, enabling construction of one-step bias-corrected estimators with appealing large-sample properties and allowing machine learning for nuisance estimation. We demonstrate that in randomized studies, one-step estimation of SMDs is robust to arbitrary nuisance model misspecification, whereas the KL divergence and broader density ratio estimands are more sensitive to nuisance misspecification. Additionally, we provide a sensitivity analysis framework that partially relaxes a monotonicity assumption required for identification, and construct one-step estimators for the resulting bounds. Through simulation studies, we demonstrate the finite-sample performance of the proposed estimators and illustrate how divergence measures can improve the interpretability of survivor analyses. Finally, we assess covariate divergence in survivor strata over time in amyotrophic lateral sclerosis (ALS) trials, using data from the Pooled Resource Open-Access ALS Clinical Trials database.
