Shape-Preserving Covariate Adjustment via Empirical Likelihood in Randomized Experiment

2026-08-19

AI Summary

本文提出了一种基于经验似然和协变量平衡约束的方法，在随机实验中调整协变量的同时保持估计量的单调性，提高了估计效率。

Abstract

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.

Problem

• Covariate Adjustment
• Monotonicity
• Empirical Likelihood
• Randomized Experiment
• Distribution Function

Innovation

• empirical likelihood
• covariate adjustment
• shape preservation
• randomized experiments
• asymptotic normality