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UID:20250512T135829EDT-1171xRfWDo@132.216.98.100
DTSTAMP:20250512T175829Z
DESCRIPTION:Lara Maleyeff\, PhD\n\nPost Doctoral Fellow in Biostatistics\n	D
 ept. of Epidemiology\, Biostatistics and Occupational Health\n	ɬÀï·¬ Unive
 rsity\n\nWHEN: Wednesday\, March 13\, 2024\, from 3:30 to 4:30 p.m.\n	WHERE
 : Hybrid | 2001 ɬÀï·¬ College Avenue\, Room 1201\; Zoom \n	NOTE: Lara Male
 yeff will be presenting in-person\n\nAbstract\n\nAs with many chronic cond
 itions\, selecting the optimal treatment to patients with rheumatoid arthr
 itis is challenging. The current trial-and-error approach\, in which patie
 nts cycle through one of the many treatment options available until remiss
 ion\, leads to months or years of suboptimal disease control\, considerabl
 e loss to patient well-being\, and a burden on healthcare systems. Precisi
 on medicine\, in which patient's characteristics inform treatment decision
 s\, requires new approaches in clinical trial design and analysis. Existin
 g clinical trial designs generally focus on biomarkers which are categoriz
 ed into pre-specified subgroups\, such as sex\, and utilize a single model
  specification. Motivated by a trial studying available treatments in rheu
 matoid arthritis\, we utilize splines and Bayesian model averaging to flex
 ibly identify the region of a multi-dimensional biomarker space where trea
 tment is effective. We consider continuous biomarkers which may have a com
 plex relationship with the outcome of interest.\n\nSpeaker bio\n\nLara Mal
 eyeff is a postdoctoral fellow in biostatistics at ɬÀï·¬\, joi
 ntly supervised by Drs. Shirin Golchi and Erica Moodie. She researches pre
 cision medicine strategies for clinical trials.\n
DTSTART:20240313T193000Z
DTEND:20240313T203000Z
SUMMARY:An Adaptive Enrichment Design Using Bayesian Model Averaging for th
 e Identification of Tailoring Variables
URL:/epi-biostat-occh/channels/event/adaptive-enrichme
 nt-design-using-bayesian-model-averaging-identification-tailoring-variable
 s-355575
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