Event

David M. Kent, Prof of Medicine - Tufts University

Monday, October 15, 2018 14:30to15:30
Purvis Hall Room 25, 1020 avenue des Pins Ouest, Montreal, QC, H3A 1A2, CA

Title: Moving from Evidence-Based Medicine to Personalized Medicine: Understanding Heterogeneous Treatment Effects in the Era of Patient-Centered Care.

Abstract: David M. Kent is Director of the Tufts Predictive Analytics and Comparative Effectiveness (PACE) Center, at the Institute for Clinical Research and Health Policy Studies (ICRHPS), Tufts Medical Center, Director of the Clinical and Translational Science (CTS) MS/PhD Program, at the Sackler School of Graduate Biomedical Sciences, Tufts University and Professor of Medicine, Neurology, and CTS at Tufts Medical Center/Tufts University School of Medicine. The main research interest at PACE is to better understand and address the limitations of using group‐derived evidence as the basis for decision making in individuals. Dr. Kent is a clinician-methodologist with a broad background in clinical epidemiology with a focus on predictive modeling, individual patient data meta-analysis, and observational comparative effectiveness research. His applied research spans several fields, but is concentrated mostly in cardiovascular disease (especially stroke). In addition to this applied work, his work also addresses methodological issues in how to employ risk-modeling approaches to clinical trial analysis to better understand heterogeneous treatment effect (HTE). Dr. Kent is currently PI of several grants including 3 PCORI grants—including a recently awarded one-in-the-nation Predictive Analytics Resource Center (PARC) grant-- and an NIH U award on these topics, as well as PI of a R01 NIH comparative effectiveness project which examines silent brain infarction through natural language processing and big data. In addition, a considerable portion of his time is spent educating and mentoring future clinical researchers, as Director of the CTS MS/PhD Program, Professor of Medicine at the Sackler School of Graduate Biomedical Sciences, and Director and PI of a NIH funded Training Program for Postdoctoral Trainees.Evidence is derived from groups but medical decisions are made by—and for—individual patients. Determining the best treatment for an individual patient is fundamentally different from determining the best treatment on average. In this talk, I will review fundamental limitations of using group-level data for decisions in individual patients, review limitations of conventional “one-variable-at-a-time” subgroup analyses and discuss the potential benefits of using more comprehensive subgrouping schemes that incorporate information on multiple variables, such as those based on summary variables (e.g., risk scores or effect scores derived by regression modeling). I will also review recent recommendations from a technical expert panel convened for PCORI for “predictive approaches” to analysis of heterogeneous treatment effect.

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