随机试验中基于经验似然的保形协变量调整
Shape-Preserving Covariate Adjustment via Empirical Likelihood in Randomized Experiment
中文总结 AI 辅助
该研究针对随机试验协变量调整方法无法保形的问题，提出带协变量平衡约束的经验似然方法，构建保形的协变量调整经验测度，可提升估计效率，经模拟及SURPASS-4试验应用验证了有效性。
中文摘要 AI 辅助
协变量调整可提升随机试验中的估计效率，但标准校准与增强方法应用于分布函数或生存函数时，无法保持单调性——这是估计量的基本属性。我们提出采用带协变量平衡约束的经验似然，为每个处理组构建经协变量调整的经验测度。随后，将广泛分布函数类的估计量（包括累积分布函数、生存函数、分位数及受限平均生存时间）推导为该测度的插件函数，自动继承合适的形状约束。我们建立了渐近正态性，相较于未调整估计量具有明确且可保证的效率增益。渐近分布对随机化方案不变，在简单随机化及所有满足温和平衡条件的常用协变量自适应设计下，提供统一的推断程序。该统一构造仅调整一次经验测度并从中推导所有估计量，实现了协变量调整与保形的原则性协调。模拟及SURPASS-4试验的应用证实了理论增益。
英文摘要
Covariate adjustment improves estimation efficiency in randomized experiments, but standard calibration and augmentation methods, when applied to distribution or survival functions, do not preserve monotonicity---a fundamental property of the estimand. We propose using empirical likelihood with covariate-balancing constraints to construct a covariate-adjusted empirical measure for each treatment arm. Estimators of a broad class of distributional functionals, including cumulative distribution functions, survival functions, quantiles, and restricted mean survival times, are then derived as plug-in functionals of this measure, automatically inheriting proper shape constraints. We establish asymptotic normality with an explicit, guaranteed efficiency gain over unadjusted estimators. The asymptotic distributions are invariant to the randomization scheme, providing a unified inference procedure under simple randomization and all commonly used covariate-adaptive designs satisfying a mild balancing condition. This unified construction, adjusting the empirical measure once and deriving all estimators from it, offers a principled reconciliation of covariate adjustment with shape preservation. Simulations and an application to the SURPASS-4 trial confirm the theoretical gains.
