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UID:20260601T192420EDT-0866GzGgJf@132.216.98.100
DTSTAMP:20260601T232420Z
DESCRIPTION:\n	\n		\n			\n				TITRE / TITLE\n					Estimating individualized treatment rule
 s without individual data in multicentre studies\n					\n					RÉSUMÉ / ABSTRACT\n\n				E
 stimating individualized treatment rules is challenging\, as the treatment
  effect heterogeneity of interest often suffers from low power. This motiv
 ates the use of very large datasets such as those from multiple health sys
 tems or multicentre studies\, which may raise concerns of data privacy. In
  this talk\, I will introduce a statistical framework for of estimation in
 dividualized treatment rules and show how distributed regression can be us
 ed in combination with dynamic weighted regression to find an optimal indi
 vidualized treatment rule whilst obscuring individual-level data. The robu
 stness of this approach and its flexibility to address local treatment pra
 ctices will be shown in simulation. The work is motivated by\, and illustr
 ated with\, an analysis of the U.K.’s Clinical Practice Research Datalink 
 on the treatment of depression.\n\n				LIEU / PLACE\n					CRM\, Salle / Room 6214\,
  Pavillon André Aisenstadt\n					Une réception suivra au salon Maurice-Labbé (s
 alle 6245)\n					A reception will follow in the Maurice-Labbé lounge (room 6245
 )\n					\n					ZOOM\n					https://us06web.zoom.us/j/84226701306?pwd=UEZ5NVpZaUlldW5qNU8vZ
 zIvbEJXQT09\n					ID: 842 2670 1306 / CODE: 692788\n					\n					ORGANISATEURS / ORGANIZER
 S\n					Erica Moodie (91ºÚÁÏÍø)\n					Giovanni Rosso (Concordia University)
 \n					Alina Stancu (Concordia University)\n					Hugh R. Thomas (Université du Québe
 c à Montréal)\n					Guy Wolf (Université de Montréal)\n			\n		\n	\n\n
DTSTART:20230512T193000Z
DTEND:20230512T203000Z
SUMMARY:Erica E. M. Moodie (Université 91ºÚÁÏÍø)
URL:/mathstat/channels/event/erica-e-m-moodie-universi
 te-mcgill-348265
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