提出统一因果框架，精准评估治疗方案的获益-风险排序优劣。
A Unified Causal Inference Framework for the Desirability of Outcome Ranking Paradigm in Benefit-Risk Evaluation
- 基于双重稳健估计，通过序数结局分布建模提升评估精度。
- TMLE-SL在偏差、置信区间覆盖率等指标上表现最优。
- 适用于随机试验与观察性研究中的药物疗效风险综合评估。
我们构建了一个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治疗方案的获益-风险排序优劣（DOOR）概率。该框架将DOOR概率表示为两种治疗策略下边际序数结局分布的双线性函数，通过序列风险集危险率估计条件序数分布，并推导出DOOR概率的高效影响函数（EIF）。点估计模拟比较了G-computation、标准化逆概率加权（IPW）、增广IPW（AIPW）和目标最大似然估计（TMLE），其中无偏函数使用广义线性模型或Super Learner（SL）估计。结果显示，TMLE-SL在点估计性能上最强且最稳定，其次为AIPW-SL。随后评估了基于EIF的推断方法在不同重叠性、治疗效应异质性和治疗分配条件下，使用与不使用交叉拟合的AIPW-SL与TMLE-SL表现。交叉验证目标最大似然估计（CVTMLE-SL）在偏差、底层序数分布恢复、标准误准确性和置信区间覆盖方面均表现最佳。方法通过抗菌耐药领导集团多药耐药生物体网络数据进行实例演示。
原文摘要 · Abstract (English)
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.
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