发表机构
The George Washington University; The Biostatistics Center, The George Washington University; AbbVie Inc.（乔治·华盛顿大学; 乔治·华盛顿大学生物统计中心; 艾伯维公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出统一协变量调整因果推断框架，用于估计DOOR概率，通过模拟比较多种方法后发现CVTMLE-SL在DOOR相关指标上整体性能最优，并结合多重耐药菌网络数据验证方法。
AI 中文摘要
我们开发了一种统一的协变量调整因果推断框架，用于估计随机试验和观察性研究中获益-风险评估的结果排序合意性（DOOR）概率。该框架将DOOR概率表示为两种治疗策略下边际有序结局分布的双线性泛函，通过序列风险集 hazard 估计条件有序分布，并推导DOOR概率的有效影响函数（EIF）。点估计模拟比较了G计算法、归一化逆概率加权（IPW）、增广IPW（AIPW）和目标最大似然估计（TMLE），其中干扰函数使用广义线性模型或Super Learner（SL）估计。TMLE-SL表现出最强且最一致的点估计性能，AIPW-SL排名第二。随后评估了基于EIF的AIPW-SL和TMLE-SL在有无交叉拟合情况下，随重叠度、治疗效应异质性和治疗分配变化的多种设置中的表现。CVTMLE-SL在DOOR尺度偏差、潜在有序分布的恢复、标准误准确性和置信区间覆盖方面表现出最强的整体性能。我们使用抗菌药物耐药性领导小组的多重耐药菌网络数据说明该方法。
英文摘要
We developed a unified covariate-adjusted causal inference framework for estimating the desirability of outcome ranking (DOOR) probability for benefit-risk evaluation in randomized trials and observational studies. The framework expresses the DOOR probability as a bilinear functional of the marginal ordinal outcome distributions under the two treatment strategies, estimates conditional ordinal distributions through sequential risk-set hazards, and derives the efficient influence function (EIF) of the DOOR probability. The point-estimation simulations compared G-computation, normalized inverse probability weighting (IPW), augmented IPW (AIPW), and targeted maximum likelihood estimation (TMLE), with nuisance functions estimated using generalized linear models or Super Learner (SL). TMLE-SL showed the strongest and most consistent point-estimation performance, with AIPW-SL ranking second. EIF-based inference was then evaluated for AIPW-SL and TMLE-SL, with and without cross-fitting, across settings varying in overlap, treatment-effect heterogeneity, and treatment allocation. CVTMLE-SL showed the strongest overall performance across DOOR-scale bias, recovery of the underlying ordinal distributions, standard-error accuracy, and confidence-interval coverage. We illustrate the methodology using data from the multidrug-resistant organism network of the Antibacterial Resistance Leadership Group.
