PDF(Pubmed)
Abstract：
The results from many cardiovascular (CV) outcome trials suggest that glucose lowering medications (GLMs) are effective for the CV clinical risk management of type 2 diabetes (T2D) patients. The aim of this study is to compare the effectiveness of two GLMs (SGLT2i and GLP-1RA) for the CV clinical risk management of T2D patients in a real-world setting, by simultaneously reducing glycated hemoglobin, body weight, and systolic blood pressure. Data from the real-world Italian multicenter retrospective study Dapagliflozin Real World evideNce in Type 2 Diabetes (DARWINT 2D) are analyzed. Different statistical approaches are compared to deal with the real-world-associated issues, which can arise from model misspecification, nonrandomized treatment assignment, and a high percentage of missingness in the outcome, and can potentially bias the marginal treatment effect (MTE) estimate and thus have an influence on the clinical risk management of patients. We compare the logistic regression (LR), propensity score (PS)-based methods, and the targeted maximum likelihood estimator (TMLE), which allows for the use of machine learning (ML) models. Furthermore, a simulation study is performed, resembling the structure of the conditional dependencies among the main variables in DARWIN-T2D. LR and PS methods do not underline any difference in the effectiveness regarding the attainment of combined CV risk factor goals between the two treatments. TMLE suggests instead that dapagliflozin is significantly more effective than GLP-1RA for the CV risk management of T2D patients. The results from the simulation study suggest that TMLE has the lowest bias and SE for the estimate of the MTE.
摘要：
许多心血管（CV）结局试验的结果表明，降糖药物（GLMs）对2型糖尿病（T2D）患者的CV临床风险管理有效。这项研究的目的是比较两种GLM（SGLT2i和GLP-1RA）在现实世界中对T2D患者进行CV临床风险管理的有效性。通过同时减少糖化血红蛋白，体重,还有收缩压.分析了来自现实世界的意大利多中心回顾性研究Dapagliflozin2型糖尿病（DARWINT2D）现实世界中的数据。比较了不同的统计方法来处理与现实世界相关的问题，这可能是由于模型错误指定，非随机治疗分配，结果中的错误比例很高，并且可能会使边际治疗效果（MTE）估计产生偏差，从而影响患者的临床风险管理。我们比较了逻辑回归(LR)，基于倾向评分(PS)的方法，和目标最大似然估计器(TMLE)，这允许使用机器学习(ML)模型。此外，进行了模拟研究，类似于DARWIN-T2D中主要变量之间的条件依赖关系的结构。LR和PS方法没有强调两种治疗方法之间在实现组合CV风险因素目标方面的有效性的任何差异。TMLE建议达格列净对于T2D患者的CV风险管理比GLP-1RA更有效。模拟研究的结果表明，TMLE对MTE的估计具有最低的偏差和SE。
