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UID:20250712T154102EDT-8867uSzBz5@132.216.98.100
DTSTAMP:20250712T194102Z
DESCRIPTION:Title:聽Non-Collapsibility: The Root of All Evil When Estimating
  and Interpreting Marginal Hazard Ratios\n\nAbstract:聽Dr Schuster accompli
 shed his early academic and professional education at the Ludwig Maximilia
 n University (LMU) of Munich and the Institute for Medical Statistics and 
 Epidemiology at the Technical University of Munich (TUM). He obtained his 
 doctorate in Biostatistics from the Faculty of Mathematics\, Informatics a
 nd Statistics at the LMU. Subsequently\, he received a post-doctoral award
  from the Canadian Network of Observational Drug Effect Studies (CNODES) a
 nd carried out a post-doctoral fellowship in pharmacoepidemiology at the D
 epartment of Epidemiology\, Biostatistics and Occupational Health\, 9I制作厂免费
  University and the Centre for Clinical Epidemiology\, Lady Davis Institut
 e for Medical Research in Montreal. He continued with a research fellowshi
 p at the Murdoch Children鈥檚 Research Institute in Melbourne where he was a
 cting Director of Biostatistics at the newly established Melbourne Childre
 n鈥檚 Trial Centre in 2015. In August 2016\, Dr Schuster started a tenure-tr
 ack faculty position as Assistant Professor at the Department of Family Me
 dicine. He is holder of a Tier II Canada Research Chair in Biostatistical 
 Methods for Primary Health Care Research. Dr Schuster鈥檚 main methodologica
 l interests are in the development and application of causal inference met
 hods for the design and analysis of cluster randomized controlled trials a
 nd observational research studies based on administrative or electronic me
 dical/health record data. For more info please visit: https://www.mcgill.c
 a/familymed/tibor-schusterIn time-to-event or survival analysis\, the Cox 
 proportional hazard model is a widely used approach for estimating relativ
 e exposure effects. The effect parameter of interest is the (log) hazard r
 atio with respect to exposure status\, conditional on covariates being inc
 luded in the model for the purpose of confounding control. Similar to the 
 odds ratio\, the hazard ratio is a non-collapsible effect measure. Non-col
 lapsibility implies that the effect parameter is not the same for differen
 t sets of covariates that are conditioned on\, even if these covariates ar
 e independent of the exposure. Furthermore\, the conditional hazard ratio 
 differs to the marginal hazard ratio that has\, under certain assumptions\
 , a causal interpretation. In my talk\, I will elaborate on the formal rel
 ationship between the conditional and marginal hazard ratio and associated
  incompatibilities regarding the proportional hazards assumption. I will p
 rovide surprising insights on how censoring does affect the magnitude of t
 he estimated marginal hazard ratio and demonstrate that the degree of cens
 orship diminishes non-collapsibility effects.\n
DTSTART:20190219T203000Z
DTEND:20190219T213000Z
LOCATION:Room 24\, Purvis Hall\, CA\, QC\, Montreal\, H3A 1A2\, 1020 avenue
  des Pins Ouest
SUMMARY:Tibor Schuster\, Phd\, 9I制作厂免费
URL:/mathstat/channels/event/tibor-schuster-phd-mcgill
 -university-294773
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