AI 中文总结
本文提出一种使用稳定平衡权重的简化协变量调整方法，可直接应用于临床试验中多种估计量，能提升渐近效率并保留特定统计关系，便于实际应用。
AI 中文摘要
协变量调整可提高临床试验中治疗效应估计的精度，监管指南越来越鼓励其实施。尽管有诸多益处，但协变量调整可能需要高级统计技术或针对不同估计量的定制方法，这可能阻碍其在实践中的应用。为解决该问题，我们引入一种使用稳定平衡权重的简化协变量调整方法，该方法可直接应用于多个临床试验估计量，包括平均治疗效应、相对风险、Mann-Whitney估计量和生存比。更准确地说，我们的结果涵盖任何作为组特异性分布的Hadamard可微泛函的估计量。一旦获得权重，只要软件能处理观测权重，我们的调整估计量可使用用于未调整分析的相同软件实现。该构造相比未调整估计提高了渐近效率，并保留了组特异性边际与亚组特异性汇总之间的插件关系。
英文摘要
Covariate adjustment can improve precision in estimating treatment effects in clinical trials, and regulatory guidance increasingly encourages its implementation. Despite its benefits, covariate adjustment can require advanced statistical techniques or tailored approaches for different estimands, which can deter its use in practice. To address this issue, we introduce a simplified approach to covariate adjustment using stable balancing weights that applies directly across many clinical trial estimands, including the average treatment effect, relative risk, Mann-Whitney estimand, and survival ratio. More precisely, our results cover any estimand that is a Hadamard differentiable functional of the arm-specific distributions. Once the weights are obtained, our adjusted estimator can be implemented with the same software used for an unadjusted analysis, provided that software can take observation weights. This construction improves asymptotic efficiency relative to unadjusted estimation and preserves the plug-in relationship between arm-specific marginal and subgroup-specific summaries.
Comments66 pages, 6 figures
