BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20251008T170147EDT-4480dNh6Vv@132.216.98.100 DTSTAMP:20251008T210147Z DESCRIPTION:Title: A Simulation-based Framework for Pragmatic Trial Design Using Observational Data.\n\nAbstract: Design of clinical trials requires careful decision-making across several dimensions\, including endpoints\, eligibility criteria\, and subgroup enrichment. Clinical trial simulation can be an informative tool in trial design\, providing empirical evidence by which to evaluate and compare the results of hypothetical trials with v arying designs. In this paper\, we introduce a novel simulation-based appr oach using observational data to inform the design of a future pragmatic t rial. To account for likely confounding by indication\, we utilize propens ity score-adjusted models to simulate hypothetical trials under alternativ e endpoints and enrollment criteria. We apply our approach to the design o f pragmatic trials in psoriatic arthritis\, using observational data embed ded within the Tight Control of Inflammation in Early Psoriatic Arthritis study to simulate hypothetical open-label trials comparing treatment with tumor necrosis factor-α inhibitors to methotrexate. We first validate our simulations of a trial with traditional enrollment criteria and endpoints against a recently published trial. Next\, we compare simulated treatment effects in patient populations defined by traditional and broadened enroll ment criteria\, where the latter is consistent with a future pragmatic tri al. In each trial\, we also consider five candidate primary endpoints. Our results highlight how changes in the enrolled population and primary endp oints may qualitatively alter study findings and the ability to detect het erogeneous treatment effects between clinical subgroups. These considerati ons\, among others\, are important for designing a future pragmatic trial aimed at having high external validity with relevance for real-world clini cal practice. Our approach may be generalized to the study of other condit ions where existing trial data are limited or do not generalize well to re al-world clinical practice\, but where observational data are available.\n \n\nAlisa J. Stephens-Shields is an Assistant Professor of Biostatistics a t the University of Pennsylvania Perelman School of Medicine. Her research focuses on flexible and efficient analysis of data from cluster-randomize d trials and other extensions of causal inference methodology to enhance t he design and analysis of clinical trials. She also works in the developme nt of patient-reported outcomes to inform population-appropriate trial end points. Dr. Stephens-Shields collaborates in several areas\, including ped iatrics\, chronic pain\, pharmacoepidemiology\, and behavioral economics. She is a recipient of the inaugural Committee of Presidents of Statistical Societies Leadership Academy award and has held elected positions in the American Statistical Association Section on Statistics in Epidemiology and the Eastern North American Region of the International Biometrics Society . Dr. Stephens-Shields serves as an associate editor of Biostatistics and a statistical consultant for the Annals of Internal Medicine. She holds Ph .D. and A.M. degrees in biostatistics from Harvard University and a B.S. i n mathematics with minor in Spanish from the University of Maryland\, Coll ege Park.\n\nVia Zoom: https://mcgill.zoom.us/j/85978187693?pwd=WWtJZUpnb0 JXK3o5SStnOFcxK3FFUT09\n\nWeb site : www.mcgill.ca/epi-biostat-occh/news-e vents/seminars/biostatistics\n\n \n DTSTART:20211027T193000Z DTEND:20211027T203000Z SUMMARY:Alisa J. Stephens-Shields (University of Pennsylvania) URL:/mathstat/channels/event/alisa-j-stephens-shields- university-pennsylvania-334366 END:VEVENT END:VCALENDAR