Event

Causal Inference SIG and EFSPI Methods Leaders: Modern Covariate Adjustment in Clinical Trials

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Date: Tuesday 30th June 2026

Time: 13:00 - 15:00 BST | 15:00 - 17:00 CEST

Speakers: Dominic Magirr (Novartis Pharma AG), Sanne Roels (Johnson & Johnson), Kelly Van Lancker (Ghent University and Vrije Universiteit Brussel) and Jurgen Hummel (Cytel).

Who is this event intended for? Statisticians active in clinical trials.

What is the benefit of attending? Increased understanding and insights in methodology, regulatory landscape, and use for covariate adjustment in clinical trials.

Overview

This webinar provides a comprehensive overview of covariate adjustment in clinical trials, covering both regulatory foundations and recent methodological developments. The session opens with an introduction to the potential gains from covariate adjustment and discuss key recommendations from the 2023 FDA guidance. This includes considerations for both linear and non-linear models, as well as areas where further research may help refine best practices for registrational trials.

Building on this foundation, Dominic Magirr (Novartis) will review well-accepted methods for covariate adjustment, including standardization (g-computation) using generalized linear models, and covariate-adjusted extensions of the log-rank test with corresponding hazard ratio estimation. The presentation will also highlight the distinction between marginal and conditional estimands and discuss the potential role of prognostic risk scores or “supercovariates.”

The webinar then moves to recent methodological developments beyond current standard practice. Sanne Roels (Johnson & Johnson) will explore extensions such as covariate adjustment in group sequential designs, with particular attention to type I error control, and discuss the move toward data-adaptive approaches, including pre-specified strategies such as Targeted Minimum Loss-Based Estimation (TMLE) across common endpoint types.

Looking ahead, Kelly Van Lancker (Ghent University and Vrije Universiteit Brussel) will discuss promising future directions, including the use of data-adaptive and machine learning–based estimators such as TMLE and related doubly robust methods. The talk will highlight key challenges around interpretability, pre-specification, and regulatory acceptability, with particular attention to small sample settings and complex data structures such as clustered or multi-center trials. Practical considerations for balancing innovation with robustness, transparency, and trust in confirmatory analyses will also be discussed.

The session concludes with a panel discussion led by Jürgen Hummel (Cytel), bringing together regulatory, industry, and academic perspectives to reflect on current practice and future directions in covariate adjustment.

 

Registration

This event is free to attend for both Members of PSI and Non-Members. To register your place, please click here.

 

Speaker Details

Speaker

Biography

Abstract

Dominic Magirr, Novartis

Dominic is part of the Advanced Methodology and Data Science group at Novartis, where he provides methodological and technical support to clinical trial teams across a wide variety of statistical topics. 

In this presentation, I will discuss well accepted methods for covariate adjustment, including standardization (or g-computation) using generalized linear models, as well as a covariate-adjusted version of the log-rank test with a corresponding method for hazard ratio estimation. I will cover the distinction between marginal and conditional estimands and discuss the potential role of risk scores or “supercovariates”.

 

Sanne Roels, Johnson & Johnson

 

Sanne is part of the statistical modelling, methodology and consulting group at J&J. The group supports teams through implementation of statistical innovation and impactful methodology, leveraging modelling and simulation.

Sanne founded and continues to co-lead PSI/EFSPI Working group on Causal Inference.

 

In this talk, I will discuss methodological developments that go beyond what is currently generally accepted. I will discuss covariate adjustment in group sequential designs and related concerns around type I error control. Next, I will discuss the considerations of moving toward data‑adaptive methods, including pre‑specified data‑adaptive strategies such as TMLE across common endpoint types. 

 

 

Kelly Van Lancker, Ghent University and Vrije Universiteit Brussel

 

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. 

 

This talk will discuss promising future directions and highlight key pitfalls and open problems. These include the use of pre‑specified data‑adaptive and machine‑learning–based estimators such as TMLE and related doubly robust methods. While these approaches offer efficiency gains, they raise practical challenges around interpretability, pre‑specification, and regulatory acceptability. Particular attention will be paid to small‑sample settings, where asymptotic guarantees may be unreliable. The talk will also address clustered and correlated data structures, common in multi‑center trials, and their implications for covariate adjustment. The session will conclude with practical considerations on balancing methodological innovation with robustness, transparency, and trust in confirmatory analyses.

 

Jurgen Hummel, Cytel

 

Jürgen Hummel is Vice President, Innovative Statistics at Cytel, and in that role he provides statistical consultancy to integrate advanced statistical approaches into development programs.  He has been working in Biostatistics in the CRO, pharmaceutical and health care industry for more than 30 years in various project related, technical and managerial positions.  Prior to joining Cytel, Jürgen led the Statistical Methodology groups at PPD (now Thermo Fisher Scientific) and at Novo Nordisk. 

Jürgen is a member of the EFSPI Statistical Methods Leaders Group, led the PSI/EFSPI Regulatory Special Interest Group for 5 years and served on the PSI Board of Directors.  He earned the German equivalent of an MSc in mathematics and economics at Augsburg University, and he is a Chartered Statistician with the Royal Statistical Society.

 

Panel Discussion Lead


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