OBJECTIVES: To simulate long-term event rates in patients treated with empagliflozin based on hard outcomes data, without reliance on surrogate biomarkers. Empagliflozin is an approved treatment for management of high glucose in patients with type 2 diabetes.  The effect of empagliflozin in addition to standard of care (SoC) on cardiovascular (CV) morbidity and mortality was evaluated in the EMPA REG OUTCOME trial. Empagliflozin was found to significantly reduce  CV event rates (HR: 0.86; 95% CI: 0.74-0.99) and CV mortality (HR: 0.62; 95% CI: 0.47-0.77) in patients at high CV risk.  METHODS: An economic model was developed which extrapolated the outcomes of patients receiving empagliflozin plus SoC compared to SoC over the patients’ remaining lifetime in the United Kingdom setting.  Patient-level outcomes data from the EMPA REG OUTCOME trial were analyzed to generate time to event distributions for ten CV and renal outcomes, including myocardial infarction (MI), stroke, heart failure hospitalization, development of chronic kidney disease, and CV mortality.  Time-dependent survival regression analyses were performed using R. Model outcomes included costs, life years, and quality-adjusted life years (QALYs). The model was validated by comparing simulated and trial outcomes at 3 years. RESULTS: The time to event approach allowed for the direct utilization of hard outcomes data from the trial in the cost-effectiveness analyses. The composite of CV mortality, non-fatal MI, and non-fatal stroke rates were 7.3 events/100 patient-years with empagliflozin compared to 9.0 events/100 patient-years with SoC.  This translated to improvements in survival (2 years incremental difference, undiscounted) and QALYs (0.9 incremental QALYs, discounted at 3.5%).  This approach, however, is not well-suited to modeling treatment patterns resulting in conservative estimates of cost-effectiveness. CONCLUSIONS: Time-to-event modeling allows direct incorporation of trial outcome data in economic analyses, but may be limited in its ability to address treatment patterns and thus pharmacy cost offsets.