91丝瓜视频

Kelly Van Lancker, PhD

Assistant Professor in Biostatistics |
Ghent University and Vrije Universiteit Brussels

Important: This seminar will take place from 10:00-11:00 a.m. ET and will be Virtual only via Zoom

WHEN: Wednesday, January 28, 2026, from 10:00 to 11:00 a.m.
WHERE:
NOTE:聽Kelly Van Lancker will be presenting from Brussels

Abstract

The ICH E9 (R1) addendum on estimands, coupled with recent advancements in causal inference, has prompted a shift towards using model-free treatment effect estimands that are more closely aligned with the underlying scientific question. This represents a departure from traditional, model-dependent approaches where the statistical model often overshadows the inquiry itself. While this shift is a positive development, it has unintentionally led to the prioritization of an estimand's ability to perfectly answer the key scientific question over its practical learnability from data under plausible assumptions. We illustrate this by scrutinizing assumptions in the recent clinical trials literature on principal stratum estimands, demonstrating that some popular assumptions are not only implausible but often inevitably violated. We advocate for a more balanced approach to estimand formulation, one that carefully considers both the scientific relevance and the practical feasibility of estimation under realistic conditions.

Speaker Bio

Kelly Van Lancker is an assistant professor in biostatistics at Ghent University and Vrije Universiteit Brussels. She received both her master degree in mathematics and her PhD degree in Statistical Data Analysis from Ghent University. Previously, Kelly was a postdoctoral researcher at the Johns Hopkins Bloomberg School of Public Health. Her goal is to develop innovative designs and analytical techniques for drawing causal inferences in health sciences. A big part of her research focuses on more accurate and faster decision-making in randomized clinical trials by making optimal use of the available data.