BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20251224T095441EST-1599tGRpmT@132.216.98.100 DTSTAMP:20251224T145441Z DESCRIPTION:Augustine Wigle\, PhD\n\nNSERC Postdoctoral Fellow | 51³Ô¹ÏÍøUni versity \n\nWHEN: Wednesday\, January 14\, 2026\, from 3:30 to 4:30 p.m.\n WHERE: Hybrid | 2001 51³Ô¹ÏÍøCollege Avenue\, Rm 1140\; Zoom\n NOTE: Augusti ne Wigle will be presenting in-person at SPGH \n\nAbstract\n\nAn optimal I ndividualized Treatment Rule (ITR) is a function that takes a patient's ch aracteristics\, such as demographics\, biomarkers\, and treatment history\ , and outputs a treatment that is expected to give the best outcome for th at patient. When estimating ITRs from individual studies\, power to detect important treatment-covariate interactions is often low. Additionally\, a ll treatments of interest may not be compared head-to-head in a single stu dy. Network Meta-Analysis (NMA) is a method of synthesizing data from mult iple studies to estimate the relative effects of a set of treatments. Two- stage ITR NMA is an emerging technique for the estimation of ITRs that can improve power and simultaneously consider all relevant treatment options while protecting sensitive data. In the first stage\, study-specific ITRs are estimated\, and in the second stage\, the study-specific ITRs are pool ed using an NMA model. In this talk\, we propose a doubly-robust and fully Bayesian approach to estimating study-level ITRs. We also show how missin g-at-random outcomes can be accounted for in this approach. We then propos e a Bayesian NMA model for pooling the study-level ITRs that leverages the full covariance matrix of the estimates. Finally\, we use the methods to estimate an optimal ITR for major depressive disorder using data from thre e adaptive trials.\n\nSpeaker Bio\n\nAugustine Wigle currently holds an NS ERC Postdoctoral Fellowship at 51³Ô¹ÏÍø supervised by Prof. Erica Moodie in the Department of Epidemiology\, Biostatistics and Occupational Health. She is interested in Bayesian methods\, network meta-analysis\, a nd precision medicine. For more information\, please visit: https://sites. google.com/view/augustinewigle \n DTSTART:20260114T203000Z DTEND:20260114T213000Z SUMMARY:Doubly-Robust Bayesian Estimation of Individualized Treatment Rules Using Network Meta-Analysis URL:/epi-biostat-occh/channels/event/doubly-robust-bay esian-estimation-individualized-treatment-rules-using-network-meta-analysi s-369990 END:VEVENT END:VCALENDAR