双重稳健的结局排名可欲性概率估计（DOOR）方法及其在MDRO研究中的应用
Doubly Robust Estimation of Desirability of Outcome Ranking (DOOR) Probability with Application to MDRO Studies
AI总结：
本文提出双重稳健方法估计结局排名可欲性概率，用于评估多药耐药菌研究中的治疗收益与风险比。
摘要：
在观察性研究中，如果计划进行治疗比较，必须调整混杂因素。对主要终点进行粗略比较而不进行协变量调整会带来偏倚，而加入回归模型可以通过纳入不平衡的协变量来提高精度，从而帮助做出正确的推断。结局排名可欲性（DOOR）是一种以患者为中心的受益-风险评估方法，设计用于随机临床试验。然而，稳健的协变量调整方法可以进一步扩大该方法在观察性研究中的适用性。在DOOR分析中，每位参与者的结局根据预设的临床标准进行排名，其中最可欲的排名代表没有副作用的良好结局，而最不理想的排名是可能的最差临床结局。我们开发了一个因果框架，通过逆概率治疗加权方法、G-计算方法以及结合两者的双重稳健方法来估计总体层面的DOOR概率。通过模拟检验所提出方法的性能。我们还对抗菌素耐药领导集团（ARLG）中的多药耐药菌（MDRO）网络进行了因果分析，比较单药治疗与联合药物治疗之间的收益：风险比。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
