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.

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Karen Tkach Tuzman, Ph.D.

Senior Editor, Head of Discovery & Preclinical Development


Jean-Frédéric Petit-Nivard

Chief Commercial Officer

Félix Balazard, Ph.D.

Lead Data Scientist

David Paulucci

Director, Data Science
Bristol Myers Squibb

Sean Khozin, M.D.

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