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Learn how using data-driven approaches to guide covariate adjustment – controlling for baseline patient variables to better estimate treatment effects – can improve clinical trials, including:
  • How covariate adjustment reduces noise in clinical trials, and the challenges of using traditional methods for covariate selection.
  • The advantages machine learning and multimodal data bring to the covariate selection process.
  • A pharma case study highlighting the impact of data-driven covariate selection on trial outcomes.
  • How regulators' appetite for data-driven covariate adjustment is changing.

Simply fill out the form to the right for complimentary access to the on-demand version of this webinar.
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Moderator
Karen Tkach Tuzman, Ph.D.


Senior Editor, Head of Discovery & Preclinical Development

BioCentury

Panelists
Jean-Frédéric Petit-Nivard


Chief Commercial Officer
Owkin

Félix Balazard, Ph.D.


Lead Data Scientist
Owkin

David Paulucci

Director, Data Science
Bristol Myers Squibb

Sean Khozin, M.D.


Chief Executive Officer, CancerLinQ LLC & Executive Vice President, ASCO, Ex FDA