Abstract

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 A (HbA ) and treatment persistence.

Results

The greater HbA reduction seen with GLA compared with LIRA (−1.39% vs. −0.74%) was primarily due to differences in baseline characteristics (HbA and endocrinologist as prescribing physician; P patients 18 to 39 years and those with HbA 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.