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UID:20251104T221333EST-145598KGws@132.216.98.100
DTSTAMP:20251105T031333Z
DESCRIPTION:Title: Moving from Evidence-Based Medicine to Personalized Medi
 cine: Understanding Heterogeneous Treatment Effects in the Era of Patient-
 Centered Care.\n\nAbstract: David M. Kent is Director of the Tufts Predict
 ive Analytics and Comparative Effectiveness (PACE) Center\, at the Institu
 te for Clinical Research and Health Policy Studies (ICRHPS)\, Tufts Medica
 l Center\, Director of the Clinical and Translational Science (CTS) MS/PhD
  Program\, at the Sackler School of Graduate Biomedical Sciences\, Tufts U
 niversity 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‐d
 erived evidence as the basis for decision making in individuals. Dr. Kent 
 is a clinician-methodologist with a broad background in clinical epidemiol
 ogy with a focus on predictive modeling\, individual patient data meta-ana
 lysis\, and observational comparative effectiveness research. His applied 
 research spans several fields\, but is concentrated mostly in cardiovascul
 ar disease (especially stroke). In addition to this applied work\, his wor
 k also addresses methodological issues in how to employ risk-modeling appr
 oaches to clinical trial analysis to better understand heterogeneous treat
 ment effect (HTE). Dr. Kent is currently PI of several grants including 3 
 PCORI grants—including a recently awarded one-in-the-nation Predictive Ana
 lytics Resource Center (PARC) grant-- and an NIH U award on these topics\,
  as well as PI of a R01 NIH comparative effectiveness project which examin
 es silent brain infarction through natural language processing and big dat
 a. In addition\, a considerable portion of his time is spent educating and
  mentoring future clinical researchers\, as Director of the CTS MS/PhD Pro
 gram\, Professor of Medicine at the Sackler School of Graduate Biomedical 
 Sciences\, and Director and PI of a NIH funded Training Program for Postdo
 ctoral 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 t
 reatment on average. In this talk\, I will review fundamental limitations 
 of using group-level data for decisions in individual patients\, review li
 mitations of conventional “one-variable-at-a-time” subgroup analyses and d
 iscuss the potential benefits of using more comprehensive subgrouping sche
 mes that incorporate information on multiple variables\, such as those bas
 ed on summary variables (e.g.\, risk scores or effect scores derived by re
 gression modeling). I will also review recent recommendations from a techn
 ical expert panel convened for PCORI for “predictive approaches” to analys
 is of heterogeneous treatment effect.\n
DTSTART:20181015T183000Z
DTEND:20181015T193000Z
LOCATION:Room 25\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:David M. Kent\, Prof of Medicine - Tufts University
URL:/mathstat/channels/event/david-m-kent-prof-medicin
 e-tufts-university-290542
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