The Use of Decomposition Methods in Real-World Treatment Benefits Evaluation for Patients with Type 2 Diabetes Initiating Different Injectable Therapies: Findings from the INITIATOR Study

Abstract

BackgroundDetermining characteristics of patients likely to benefit from a particular treatment could help physicians set personalized targets. ObjectivesTo use decomposition methodology on real-world data to identify the relative contributions of treatment effects and patients’ baseline characteristics. MethodsDecomposition analyses were performed on data from the Initiation of New Injectable Treatment Introduced after Antidiabetic Therapy with Oral-only Regimens (INITIATOR) study, a real-world study of patients with type 2 diabetes started on insulin glargine (GLA) or liraglutide (LIRA). These analyses investigated relative contributions of differences in baseline characteristics and treatment effects to observed differences in 1-year outcomes for reduction in glycated hemoglobin A1c (HbA1c) and treatment persistence. ResultsThe greater HbA1c reduction seen with GLA compared with LIRA (−1.39% vs. −0.74%) was primarily due to differences in baseline characteristics (HbA1c and endocrinologist as prescribing physician; P < 0.050). Patients with baseline HbA1c of 9.0% or more or evidence of diagnosis codes related to mental illness achieved greater HbA1c reductions with GLA, whereas patients with baseline polypharmacy (6–10 classes) or hypogylcemia achieved greater reductions with LIRA. Decomposition analyses also showed that the higher persistence seen with GLA (65% vs. 49%) was mainly caused by differences in treatment effects (P < 0.001). Patients 65 years and older, those with HbA1c of 9.0% or more, those taking three oral antidiabetes drugs, and those with polypharmacy of more than 10 classes had higher persistence with GLA; patients 18 to 39 years and those with HbA1c of 7.0% to less than 8.0% had higher persistence with LIRA. ConclusionsAlthough decomposition does not demonstrate causal relationships, this method could be useful for examining the source of differences in outcomes between treatments in a real-world setting and could help physicians identify patients likely to respond to a particular treatment.