Characteristics of Patients With Type 2 Diabetes Mellitus Receiving Treatment With Dapagliflozin Versus Sitagliptin: An Analysis of Commercial Claims and Linked Laboratory Data

Study Summary

Rationale: In order to compare the real-world use of commonly prescribed second-line oral diabetes therapies, real-world data comparing patients receiving treatment with dapagliflozin vs sitagliptin are needed, and limited resource use and cost data exist for patients initiating dapagliflozin in the real-world setting. Objectives: Primary: The primary objective of this study is to compare real-world health care resource utilization and costs following treatment with dapagliflozin versus sitagliptin. Secondary: The secondary objectives of this study are to assess clinical outcomes and treatment patterns among patients receiving treatment with dapagliflozin versus sitagliptin. Study Design: Retrospective cohort study. Target Subject Population: Patients receiving treatment with either dapagliflozin or sitagliptin will be evaluated. Study Variable(s): Primary Variables: Health care resource utilization and costs Secondary Variables: Demographics measured at the index date, Quan-Charlson Comorbidity score, measured during the baseline period, Agency for Healthcare Research and Quality (AHRQ)-based comorbidity measures, receipt of other antidiabetic medication classes during the baseline and follow-up periods, number and percentage of patients with a diagnosis of obesity during the baseline or follow-up periods, treatment patterns during the follow-up period (i.e., index dose, duration of treatment, discontinuation, adherence, receipt of additional antidiabetic medication classes), HbA1c outcomes (among subgroup of patients with linked laboratory data, sample size permitting), renal impairment, hypoglycemia as defined by a claims-based algorithm. Statistical Methods: Initial analyses will be descriptive in nature and entail the tabular display of mean values, medians, ranges, and standard deviations of continuous variables of interest (e.g., patient age) and frequency distributions for categorical variables (e.g., sex, geographic location). Outcomes will be compared