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DESCRIPTION:\n				TITRE / TITLE\n					Estimating individualized treatment rules with
 out individual data in multicentre studiesRÉSUMÉ / ABSTRACT\n\n				Estimating 
 individualized treatment rules is challenging\, as the treatment effect he
 terogeneity of interest often suffers from low power. This motivates the u
 se of very large datasets such as those from multiple health systems or mu
 lticentre studies\, which may raise concerns of data privacy. In this talk
 \, I will introduce a statistical framework for of estimation individualiz
 ed treatment rules and show how distributed regression can be used in comb
 ination with dynamic weighted regression to find an optimal individualized
  treatment rule whilst obscuring individual-level data. The robustness of 
 this approach and its flexibility to address local treatment practices wil
 l be shown in simulation. The work is motivated by\, and illustrated with\
 , an analysis of the U.K.’s Clinical Practice Research Datalink on the tre
 atment of depression.\n\n				LIEU / PLACE\n					CRM\, Salle / Room 6214\, Pavillon 
 André AisenstadtUne réception suivra au salon Maurice-Labbé (salle 6245)A 
 reception will follow in the Maurice-Labbé lounge (room 6245)ZOOMhttps://u
 s06web.zoom.us/j/84226701306?pwd=UEZ5NVpZaUlldW5qNU8vZzIvbEJXQT09\n					ID: 842
  2670 1306 / CODE: 692788ORGANISATEURS / ORGANIZERS\n					Erica Moodie (McGill 
 University)\n					Giovanni Rosso (Concordia University)\n					Alina Stancu (Concordi
 a University)\n					Hugh R. Thomas (Université du Québec à Montréal)\n					Guy Wolf 
 (Université de Montréal)\n			\n		
DTSTART:20230512T193000Z
DTEND:20230512T203000Z
SUMMARY:Erica E. M. Moodie (Université McGill)
URL:https://www.mcgill.ca/channels/channels/event/erica-e-m-moodie-universi
 te-mcgill-348265
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