发表机构
New York University Grossman School of Medicine（纽约大学格罗斯曼医学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出非参数去偏机器学习估计器，用于将随机对照试验的生存治疗效果迁移泛化到目标人群，推导有效影响函数并实现双重稳健与半参数效率，模拟和实例验证了其性能。
AI 中文摘要
随机对照试验提供了内部有效的治疗效果估计，但其结果可能因基线协变量分布、治疗依从性或结局机制的差异而无法直接应用于更广泛的目标人群。在标准的迁移和事件时间可识别性假设下，我们开发了非参数、去偏的机器学习估计器，用于在离散时间下将因果生存治疗效果差异从源人群迁移和泛化到目标人群。我们推导了迁移和泛化生存差异估计量的有效影响函数，并提出了交叉拟合的一步估计器，这些估计器具有双重稳健性，并在弱正则条件下达到半参数效率界。我们进一步引入了利用已知效应修饰子集的估计器，通过对生存函数进行加性参数化，降低了重加权的维度，从而获得更小或相等的渐近方差。我们为所有提出的估计器建立了渐近正态性、双重稳健性和收敛速率。通过蒙特卡洛模拟，在灵活和错误设定的扰动估计场景下展示了有限样本性质。我们将这些方法应用于妇女健康倡议的数据，以估计激素治疗对冠心病在试验和观察人群中的效果。
英文摘要
Randomized controlled trials provide internally valid estimates of treatment effects, but their results may not directly apply to broader target populations due to differences in baseline covariate distributions, adherence to treatment or variations in outcome mechanisms. Under standard transport and time-to-event identifiability assumptions, we develop nonparametric, debiased machine learning estimators for transporting and generalizing causal survival treatment effect differences from a source population to a target population in discrete time. We derive the efficient influence functions for the transport and generalization survival difference estimands and propose cross-fitted one-step estimators that are doubly robust and achieve semiparametric efficiency bounds under weak regularity conditions. We further introduce estimators that exploit known effect modifier subsets through an additive parameterization of the survival function, reducing the dimensionality of the reweighting and yielding smaller or equal asymptotic variance. We establish asymptotic normality, double robustness, and rates of convergence for all proposed estimators. Finite-sample properties are illustrated through Monte Carlo simulations under flexible and misspecified nuisance estimation scenarios. We apply the methods to data from the Women's Health Initiative to estimate the effect of hormone therapy on coronary heart disease across trial and observational populations.
