Introduction The LIGHTNING study applied conventional and advanced analytic approaches to model, predict, and compare hypoglycemia rates of people with type 2 diabetes (T2DM) on insulin glargine 300 U/ml (Gla-300) with those on first-generation (insulin glargine 100 U/ml [Gla-100]; insulin detemir [IDet]) or second-generation (insulin degludec [IDeg]) basal-insulin (BI) analogs, utilizing a large real-world database. Methods Data were collected between 1 January 2007 and 31 March 2017 from the Optum Humedica US electronic health records [EHR] database. Patient-treatments, the period during which a patient used a specific BI, were analyzed for patients who switched from a prior BI or those who newly initiated BI therapy. Data were analyzed using two approaches: propensity score matching (PSM) and a predictive modeling approach using machine learning. Results A total of 831,456 patients with T2DM receiving BI were included from the EHR data set. Following selection, 198,198 patient-treatments were available for predictive modeling. The analysis showed that rates of severe hypoglycemia (using a modified definition) were approximately 50% lower with Gla-300 than with Gla-100 or IDet in insulin-naïve individuals, and 30% lower versus IDet in BI switchers (all p