595 Is PHASTR faster? A target trial emulation case study in the N3C - Steve Johnson; Carolyn Bramante; Til Stürmer; John Buse; Talia Wiggen; Jared Huling; Andrew Toler (Journal of Clinical and Translational Science)
Objectives/Goals: Our study team won a Public Health Answers to Speed Tractable Results (PHASTR) contract to conduct a target trial emulation to answer “Does metformin show a reduction of severe outcomes of COVID-19 or of Long COVID in the N3C Data Enclave?” We quickly delivered an answer due to productive technical and collaboration support in the N3C. Methods/Study Population: Our analytic plan was updated based on helpful feedback from the PHASTR program. We performed a trial emulation analysis using the N3C data, comparing adult new users of metformin to controls prescribed fluvoxamine, fluticasone, ivermectin, or montelukast. The composite outcome was Long COVID or Death (LC/D) within 180 days of COVID infection. We used entropy balancing to estimate the average treatment effect with a weighted log-linear model. Productivity was enhanced by reusing code workbooks and validated codesets from related N3C projects. The team of 4 (physician, informaticist, data programmer, and statistician) and key unpaid advisors spent 10 weeks developing and analyzing the data. Results/Anticipated Results: Totally, 9,660 patients were identified for analysis. After weighting, there were 248 in the metformin and control groups. In the metformin group, 4.0% developed LC/D vs. 8.5% in the control group, with an adjusted risk ratio (aRR) of 0.47 (95% CI 0.25 to 0.89). Results were consistent across subgroups and sensitivity analyses. The PHASTR contract structure helped produce high-quality results quickly by not only providing funding but also requiring a compressed timeline for a small team to focus on the study. The most time was spent on contract execution, enclave provisioning, and too many last-minute download requests. A project final report was submitted in March and a full manuscript was submitted in September. Discussion/Significance of Impact: The analysis was productive because the environment made reuse easy and supported rich collaborations among clinicians, informaticists, epidemiologists, statisticians, and data developers. Advice from PHASTR advisors (Axel) and N3C diabetes domain team members was also key to a faster completion.