Using real-world data to predict findings of an ongoing phase IV trial: glycemic control of semaglutide versus standard of care
Sushama Kattinakere Sreedhara, Sebastian Schneeweiss, Elvira D’Andrea, Janick G Weberpals, Elyse C DiCesare, Elisabetta Patorno, Theodore Tsacogianis, Marie Bradley, John Concato, Shirley V Wang

TL;DR
This study used real-world data to predict the results of a trial comparing semaglutide to standard diabetes treatments, finding semaglutide more effective at improving glycemic control.
Contribution
The study demonstrates that real-world data can predict ongoing trial outcomes and complement randomized trials.
Findings
Semaglutide initiators were 30% more likely to achieve A1C <7% compared to standard of care.
Semaglutide led to a 1.3% A1C reduction versus 1.1% with standard of care medications.
Abstract
Using national claims databases, we sought to emulate the design of the ongoing SEPRA trial and predict its findings, comparing the effects of once weekly semaglutide to SoC medications on glycemic control (A1C <7%) in type-2 diabetes mellitus (T2D). Using Optum Clinformatics (July 2017 – May 2022), we identified a 1:1 propensity score-matched (PSM) cohort of adults with T2D on metformin monotherapy, who had recorded A1C and initiated either injectable semaglutide or SoC medications (dipeptidyl peptidase-4 inhibitors, sodium-glucose cotransporter-2 inhibitors, SUs, or glucagon-like peptide-1 agonists) and met eligibility criteria adapted from the SEPRA trial. The primary outcome was the proportion of patients achieving A1C <7%. The study protocol was preregistered (NCT05577728, ClinicalTrials.gov) before any etiologic analyses. Risk ratios and corresponding 95% CIs were estimated. We…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Advanced Causal Inference Techniques · Diabetes Treatment and Management
