Event

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

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Date: Tuesday 30th June 2026

Time: 14:00 - 16:00 BST | 15:00 - 17:00 CEST

Speakers: Dr Daniel Rubin (FDA), Dominic Magirr (Novartis Pharma AG), Sanne Roels (Johnson & Johnson), Marlena Bannick (Fred Hutchinson Cancer Center (Incoming); University of Washington), 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, Marlena Bannick (Fred Hutchinson Cancer Center (Incoming); University of Washington) will link these future directions with practical implementation. The talk will introduce the RobinCar Family of R packages and discuss the use of machine learning, handling of missing data, and adjustments for small sample sizes. The talk will also discuss promising future directions including covariate adjustment for time-to-event outcomes and how covariate adjustment can be implemented in trials with complex and innovative designs.

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

Dr Daniel Rubin

FDA

Daniel Rubin is a statistician at the US Food and Drug Administration. He began working at FDA in 2009 after receiving his PhD in biostatistics from University of California, Berkeley. In his career he has focused on the design and analysis of clinical trials for anti-infective drugs. He has worked on the  development of multiple FDA guidance documents,  including leading the working group that drafted FDA's 2023 guidance on adjusting for covariates in  randomized clinical trials for drugs and biological products.

 

This talk will give an overview of the potential gains from covariate adjustment for the analysis of clinical trials. Recommendations in the 2023 FDA guidance document on covariate adjustment will be discussed, including considerations for covariate adjustment with linear models and nonlinear models. The talk will also discuss several areas where additional research may inform best practices for covariate adjustment in registrational trials. 

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. 

Marlena Bannick

Fred Hutchinson Cancer Center (Incoming); University of Washington

Marlena is an incoming Assistant Professor in Biostatistics at Fred Hutchinson Cancer Center. Her research focuses on robust and efficient design and analysis of randomized clinical trials, a portfolio which she developed during her time at the University of Washington. She is an active member of the ASA’s Covariate Adjustment working group.

In this talk, I will link future directions with practical implementation. I will introduce the RobinCar Family of R packages and discuss the use of machine learning, handling of missing data, and adjustments for small sample sizes. I will also discuss promising future directions including covariate adjustment for time-to-event outcomes and how covariate adjustment can be implemented in trials with complex and innovative designs.

Jürgen 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

Robin Ristl

Medical University of Vienna

 

Robin is researcher at the Center for Medical Data Science at the Medical University of Vienna. His research is focused on the design and analysis of clinical trials, with particular interest in survival analysis, regression modelling, causal inference and multiple testing procedures. He is supporting the statistical planning and analysis of academic clinical studies and provides scientific expertise to EMA as European expert.

 

  Panel Discussant


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