Assessment of Glucose Lowering Medications’ Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Misspecification and Missing Outcomes

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.