BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//132.216.98.100//NONSGML kigkonsult.se iCalcreator 2.20.4//
BEGIN:VEVENT
UID:20260124T025403EST-5760zINrfD@132.216.98.100
DTSTAMP:20260124T075403Z
DESCRIPTION:Kelly Van Lancker\, PhD\n\nAssistant Professor in Biostatistics
  |\n	Ghent University and Vrije Universiteit Brussels\n\nImportant: This se
 minar will take place from 10:00-11:00 a.m. ET and will be Virtual only vi
 a Zoom\n	\n	WHEN: Wednesday\, January 28\, 2026\, from 10:00 to 11:00 a.m.\n
 	WHERE: Virtual Seminar - Zoom only\n	NOTE: Kelly Van Lancker will be presen
 ting from Brussels\n\nAbstract\n\nThe ICH E9 (R1) addendum on estimands\, 
 coupled with recent advancements in causal inference\, has prompted a shif
 t towards using model-free treatment effect estimands that are more closel
 y aligned with the underlying scientific question. This represents a depar
 ture from traditional\, model-dependent approaches where the statistical m
 odel often overshadows the inquiry itself. While this shift is a positive 
 development\, it has unintentionally led to the prioritization of an estim
 and's ability to perfectly answer the key scientific question over its pra
 ctical learnability from data under plausible assumptions. We illustrate t
 his by scrutinizing assumptions in the recent clinical trials literature o
 n principal stratum estimands\, demonstrating that some popular assumption
 s are not only implausible but often inevitably violated. We advocate for 
 a more balanced approach to estimand formulation\, one that carefully cons
 iders both the scientific relevance and the practical feasibility of estim
 ation under realistic conditions.\n\nSpeaker Bio\n\nKelly Van Lancker is a
 n assistant professor in biostatistics at Ghent University and Vrije Unive
 rsiteit Brussels. She received both her master degree in mathematics and h
 er PhD degree in Statistical Data Analysis from Ghent University. Previous
 ly\, Kelly was a postdoctoral researcher at the Johns Hopkins Bloomberg Sc
 hool of Public Health. Her goal is to develop innovative designs and analy
 tical techniques for drawing causal inferences in health sciences. A big p
 art of her research focuses on more accurate and faster decision-making in
  randomized clinical trials by making optimal use of the available data.\n
DTSTART:20260128T150000Z
DTEND:20260128T160000Z
SUMMARY:Chasing Shadows: How Implausible Assumptions Skew Our Understanding
  of Causal Estimands
URL:/epi-biostat-occh/channels/event/chasing-shadows-h
 ow-implausible-assumptions-skew-our-understanding-causal-estimands-370522
END:VEVENT
END:VCALENDAR