Metformin And Cardiovascular Effectiveness vs SGLT2 (MACES)

Baseline Covariates: All covariates will be assessed prior to cohort entry. Covariates will reflect diagnoses and procedures recorded during health encounters, including chronic medical conditions (e.g., hypertension, coronary artery disease), diabetes severity (e.g., hemoglobin A1C, end-organ damage), overall healthcare utilization (e.g., recent hospitalization, emergency department visit), prescriber characteristics (e.g., endocrinologist, general practitioner), and medications (e.g., anti-hypertensives, diuretics). Statistical analysis Propensity score matching will be used to adjust for confounding. The probability of initiating an SGLT2-inhibitor will be calculated through a multivariable logistic regression model containing all of the baseline covariates. Using this propensity score, patients prescribed an SGLT2 were matched 1:1 with patients prescribed metformin using a caliper of up to 0.1 on the probability scale. Covariate balance between the matched cohorts was assessed using standardized differences. Since laboratory data were not available for all patients, these were not included in the propensity score estimation. After propensity score matching, proportional hazards models will be used to estimate the incidence rate, hazard ratios and 95% confidence intervals for the primary outcome without further adjustments. Schoenfeld residuals will be plotted to assess the proportional hazards assumption. Predefined sensitivity and subgroup analyses included an intention to treat analysis where the censoring criteria of drug discontinuation, switching or augmentation are removed. The investigators will also assess the primary risk in a cohort restricted to patients with a past-history of cardiovascular disease if our sample size allows it. To test the specificity of our findings, the investigators will also conduct a tracer analysis using cellulitis as an outcome, since cellulitis is not associated with SGLT2s or metformin.