Authors: Brekke L, Buysman E, Grabner M, Ke X, Xie L, Baser O, Wei W

Background: Determining characteristics of patients likely to benefit from a particular treatment could help physicians set personalized targets.

Objectives: To use decomposition methodology on real-world data to identify the relative contributions of treatment effects and patients’ baseline characteristics.

Methods: Decomposition 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.

Results: The 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.

Conclusions: Although 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.

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