BEGIN:VCALENDAR VERSION:2.0 PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4// BEGIN:VEVENT UID:20250801T122142EDT-0876hIOWCH@132.216.98.100 DTSTAMP:20250801T162142Z DESCRIPTION:Michael Wallace\, PhD\n\nPostdoctoral Fellow\, Department of Ep idemiology\, Biostatistics and Occupational Health\, ºÚÁϲ»´òìÈ University\n\n Personalizing Medicine: New Ideas for Dynamic Treatment Regimes\n\nALL ARE WELCOME\n\nAbstract:\n\nPersonalized medicine is a rapidly expanding area of health research wherein subject level information is used to inform tr eatment. Dynamic treatment regimens (DTRs) are one means by which personal ized medicine can be studied theoretically and applied in practice. DTRs a re sequences of decision rules which take subject information as input and provide treatment recommendations as output. Such regimens therefore tail or each treatment decision to a patient's unique circumstances\, but can a lso identify management plans which optimize long-term outcomes by accommo dating potentially obscure delayed treatment effects and other complex int eractions. However\, taking such factors into account can complicate the p roblem of causal inference in this context. One approach considers the bli p: a structural nested mean model of the expected difference in the (poten tially counterfactual) outcome when using a baseline treatment instead of the observed treatment. DTR estimation in this context therefore relies on estimating blip parameters and numerous methods have been proposed for th is purpose. In this talk I present an approach which uses standard weighte d ordinary least squares regression to control for the potentially confoun ding effects of covariate-dependent treatment. This builds on two establis hed methods: Q-learning and G-estimation\, offering the doubly-robust prop erty of the latter but with ease of implementation akin to the former. I'l l outline the underlying theory and demonstrate the double-robustness and efficiency properties of the approach through illustrative examples. Final ly I'll discuss model assessment\, demonstrating diagnostic plots for the method\, and how the double robustness property itself may be leveraged to investigate model validity.\n\nBio:\n\nMichael Wallace is a postdoctoral fellow in the Department of Epidemiology and Occupational Health\, working with Professor Erica Moodie (also EBOH) and Professor David Stephens (Mat hematics and Statistics). His research focuses on developing methodology f or the identification of dynamic treatment regimes: disease management pla ns that vary depending on patient-level information. Michael received his undergraduate training in mathematics at Trinity College\, Cambridge\, bef ore pursuing a Master's in statistics at University College London. His Ph D thesis\, which concerned covariate measurement error in regression model ling\, was completed at the London School of Hygiene and Tropical Medicine . Outside research\, Michael has a strong interest in promoting statistics (and statistical thinking) to those beyond the statistical community\, an d serves on the Editorial Board of the Royal Statistical Society/American Statistical Association magazine Significance.\n DTSTART:20151103T203000Z DTEND:20151103T213000Z LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue des Pins Ouest SUMMARY:Special Seminar URL:/epi-biostat-occh/channels/event/special-seminar-2 56178 END:VEVENT END:VCALENDAR