In observational studies, adjusting for confounders is required if a treatment comparison is planned. A crude comparison of the primary endpoint without covariate adjustment will suffer from biases, and the addition of regression models could improve precision by incorporating imbalanced covariates and thus help make correct inference. Desirability of outcome ranking (DOOR) is a patient-centric benefit-risk evaluation methodology designed for randomized clinical trials. Still, robust covariate adjustment methods could further expand the compatibility of this method in observational studies. In DOOR analysis, each participant's outcome is ranked based on pre-specified clinical criteria, where the most desirable rank represents a good outcome with no side effects and the least desirable rank is the worst possible clinical outcome. We develop a causal framework for estimating the population-level DOOR probability, via the inverse probability of treatment weighting method, G-Computation method, and a Doubly Robust method that combines both. The performance of the proposed methodologies is examined through simulations. We also perform a causal analysis of the Multi-Drug Resistant Organism (MDRO) network within the Antibacterial Resistant Leadership Group (ARLG), comparing the benefit:risk between Mono-drug therapy and Combination-drug therapy.

翻译：在观察性研究中，若计划进行治疗方案比较，则需对混杂因素进行调整。未经协变量调整的原始主要终点比较会存在偏倚，而引入回归模型可通过纳入不平衡协变量来提高估计精度，从而帮助做出正确推断。结局排序期望（DOOR）是一种为随机临床试验设计的以患者为中心的获益-风险评估方法。然而，稳健的协变量调整方法可进一步扩展该方法在观察性研究中的适用性。在DOOR分析中，每位受试者的结局根据预先设定的临床标准进行排序，其中最优等级代表良好结局且无副作用，最差等级则代表可能的最坏临床结局。我们通过治疗逆概率加权法、G-计算法以及结合二者的双重稳健法，建立了估计总体水平DOOR概率的因果推断框架。通过模拟研究检验了所提方法的性能。我们还对抗菌药物耐药领导组织（ARLG）内的多重耐药菌（MDRO）网络进行了因果分析，比较了单药治疗与联合治疗的获益风险比。