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UID:20250912T045922EDT-5982VANH0X@132.216.98.100
DTSTAMP:20250912T085922Z
DESCRIPTION:Dr. Schuster’s main methodological interests are in the develop
 ment and application of causal inference methods for the design and analys
 is of cluster randomized controlled trials and observational research stud
 ies based on administrative or electronic medical / health record data.\n	
 \n	Machine Learning (ML) methods are gaining increasing popularity in drug 
 safety studies using large observational databases. Applications include t
 he identification of risk factors for critical health outcomes and the cla
 ssification of patients into risk strata to optimize individual treatment 
 recommendations and surveillance over the course of treatment. Risk-modify
 ing factors can be invariant characteristics of an individual but also tim
 e-dependent exposures. A particular threat are unintended drug-drug intera
 ctions that are difficult to model using conventional data analysis approa
 ches (e.g. risk regression models) due to the complex time-dynamic nature 
 of multiple drug exposures. In his talk. Dr. Schuster will show examples o
 n how Machine Learning approaches can be used to help identifying potentia
 l risk predictors in complex data settings. He will demonstrate limitation
 s of ML approaches in situations where the temporal order of input informa
 tion (predictor candidates) is ignored and collider stratification bias wi
 ll render estimated variable importance and associated effect estimates in
 valid proxies for their causal counterparts.\n	\n	Join us afterwards for our
  'Buck-a-beer' Faculty\, Staff and Student Mixer Event\, from 4 to 6 p.m. 
 \n	* Beer will be sold at 1$ each. \n	\n	Department of Family Medicine\n	5858 
 ch. de la Côte-des-Neiges\, Suite 300\n	\n	There is no parking on site and p
 arking is limited in the area. Taxis and public transport are advised.\n	\n
 	Cannot make the seminar physically\, but would like to attend? Please join
  the webinar here.\n	(Note: Students from FMED 504 are expected to attend)
 \n
DTSTART:20190206T200000Z
DTEND:20190206T210000Z
LOCATION:CA\, QC\, Montreal\, Department of Family Medicine\, 5858 Chemin d
 e la Côte-des-Neiges\, Suite 300
SUMMARY:Research Seminar: 'No causality in - No causality out: Utility and 
 limits of machine learning in drug safety research' by Tibor Schuster\, Ph
 D
URL:/familymed/channels/event/research-seminar-no-caus
 ality-no-causality-out-utility-and-limits-machine-learning-drug-safety-294
 409
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