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

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.

AIMS SIG - Open-Source Lunch Bites

Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry. This month’s session, “Graphics Basics,” will introduce the fundamentals of producing graphics using the ggplot2 package.

PSI Book Club: Change: How organisations achieve hard-to-image results in uncertain and volatile times

Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change. Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization. Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.

PSI Book Club: Another Door Opens – Book Club Special Event

This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.

PSI Career Young Virtual Event (Q3 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Training Course: Propensity Scores: Practical Application in Non-randomised Studies

The course will introduce the topic of propensity scores and the use of external data. Covering the topics of matching and weighting as well as more advance topics of high dimension propensity scores, multi-valued treatments, double robustness and time-varying scenarios. There will be the opportunity to participate in some hands on practical exercises in R.

PSI Career Young Virtual Event (Q4 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

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

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

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

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

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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