BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:GMT Standard Time BEGIN:STANDARD DTSTART:20251002T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=2;BYMINUTE=0;BYMONTH=10 TZNAME:GMT Standard Time TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20250301T010000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYHOUR=1;BYMINUTE=0;BYMONTH=3 TZNAME:GMT Daylight Time TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Tuesday 30th June 2026Time: \;14:00 - 16:00 BST | 15: 00 - 17:00 CESTSpeakers: \;Dominic Magirr (Novartis Pharma AG)\, \ ;Sanne Roels (Johnson &\; Johnson)\, \;Kelly Van Lancker (Ghent Uni versity and Vrije Universiteit Brussel) and \;Jurgen Hummel (Cytel).Wh o is this event intended for? Statisticians active in clinical trials.What is the benefit of attending? Increased understanding and insights in meth odology\, regulatory landscape\, and use for covariate adjustment in clini cal trials.OverviewThis webinar provides a comprehensive overview of covar iate adjustment in clinical trials\, covering both regulatory foundations and recent methodological developments. The session opens with an introduc tion to the potential gains from covariate adjustment and discuss key reco mmendations from the 2023 FDA guidance. This includes considerations for b oth 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 wil l also highlight the distinction between marginal and conditional estimand s and discuss the potential role of prognostic risk scores or &ldquo\;supe rcovariates.&rdquo\;The webinar then moves to recent methodological develo pments beyond current standard practice. Sanne Roels (Johnson &\; Johns on) will explore extensions such as covariate adjustment in group sequenti al designs\, with particular attention to type I error control\, and discu ss the move toward data-adaptive approaches\, including pre-specified stra tegies such as Targeted Minimum Loss-Based Estimation (TMLE) across common endpoint types.Looking ahead\, Kelly Van Lancker (Ghent University and Vr ije Universiteit Brussel) will discuss promising future directions\, inclu ding the use of data-adaptive and machine learning&ndash\;based estimators such as TMLE and related doubly robust methods. The talk will highlight k ey challenges around interpretability\, pre-specification\, and regulatory acceptability\, with particular attention to small sample settings and co mplex data structures such as clustered or multi-center trials. Practical considerations for balancing innovation with robustness\, transparency\, a nd trust in confirmatory analyses will also be discussed.The session concl udes with a panel discussion led by Jü\;rgen Hummel (Cytel)\, bringing together regulatory\, industry\, and academic perspectives to reflect on current practice and future directions in covariate adjustment. \;Regi strationThis event is free to attend for both Members of PSI and Non-Membe rs. To register your place\, please click here. \;Speaker DetailsSpeak erBiographyAbstractDominic Magirr\, NovartisDominic is part of the Advance d Methodology and Data Science group at Novartis\, where he provides metho dological and technical support to clinical trial teams across a wide vari ety of statistical topics. \;In this presentation\, I will discuss wel l accepted methods for covariate adjustment\, including standardization (o r g-computation) using generalized linear models\, as well as a covariate- adjusted version of the log-rank test with a corresponding method for haza rd ratio estimation. I will cover the distinction between marginal and con ditional estimands and discuss the potential role of risk scores or &ldquo \;supercovariates&rdquo\;. \;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 stat istical innovation and impactful methodology\, leveraging modelling and si mulation.Sanne founded and continues to co-lead PSI/EFSPI Working group on Causal Inference. \;In this talk\, I will discuss methodological deve lopments that go beyond what is currently generally accepted. I will discu ss covariate adjustment in group sequential designs and related concerns a round type I error control. Next\, I will discuss the considerations of mo ving 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& nbsp\;Kelly Van Lancker is an assistant professor in biostatistics at Ghen t University and Vrije Universiteit Brussels. She received both her master degree in mathematics and her PhD degree in Statistical Data Analysis fro m Ghent University. \; Previously\, Kelly was a postdoctoral researche r at the Johns Hopkins Bloomberg School of Public Health. Her goal is to d evelop innovative designs and analytical techniques \;for drawing caus al inferences in health sciences. A big part of her research focuses on mo re accurate and faster decision-making in randomized clinical trials by ma king optimal use of the available data. \; \;This talk will discus s promising future directions and highlight key pitfalls and open problems . These include the use of pre‑specified data‑adaptive and machine‑learnin g&ndash\;based estimators such as TMLE and related doubly robust methods. While these approaches offer efficiency gains\, they raise practical chall enges around interpretability\, pre‑specification\, and regulatory accepta bility. Particular attention will be paid to small‑sample settings\, where asymptotic guarantees may be unreliable. The talk will also address clust ered and correlated data structures\, common in multi‑center trials\, and their implications for covariate adjustment. The session will conclude wit h practical considerations on \;balancing methodological innovation wi th robustness\, transparency\, and trust in confirmatory analyses. \;J urgen Hummel\, Cytel \;Jü\;rgen Hummel is Vice President\, Innovat ive Statistics at Cytel\, and in that role he provides statistical consult ancy to integrate advanced statistical approaches into development program s. \; He has been working in Biostatistics in the CRO\, pharmaceutical and health care industry for more than 30 years in various project relate d\, technical and managerial positions. \; Prior to joining Cytel\, J& uuml\;rgen led the Statistical Methodology groups at PPD (now Thermo Fishe r Scientific) and at Novo Nordisk. \;Jü\;rgen is a member of the E FSPI Statistical Methods Leaders Group\, led the PSI/EFSPI Regulatory Spec ial Interest Group for 5 years and served on the PSI Board of Directors.&n bsp\; He earned the German equivalent of an MSc in mathematics and economi cs at Augsburg University\, and he is a Chartered Statistician with the Ro yal Statistical Society.\n  \;Panel Discussion Lead DTEND:20260630T150000Z DTSTAMP:20260521T174523Z DTSTART:20260630T130000Z LOCATION: SEQUENCE:0 SUMMARY:Causal Inference SIG and EFSPI Methods Leaders: Modern Covariate Ad justment in Clinical Trials UID:RFCALITEM639149823231059324 X-ALT-DESC;FMTTYPE=text/html:

Date: Tue sday 30th June 2026

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

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

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

What is the benefit of attending? Increased understan ding and insights in methodology\, regulatory landscape\, and use for cova riate adjustment in clinical trials.

Overview

This webinar p rovides a comprehensive overview of covariate adjustment in clinical trial s\, covering both regulatory foundations and recent methodological develop ments. The session opens with an introduction to the potential gains from covariate adjustment and discuss key recommendations from the 2023 FDA gui dance. This includes considerations for both linear and non-linear models\ , as well as areas where further research may help refine best practices f or registrational trials.

Building on this foundation\, Dominic Magirr (Novartis) will review well-accepted methods for covariate adjustm ent\, including standardization (g-computation) using generalized linear m odels\, and covariate-adjusted extensions of the log-rank test with corres ponding hazard ratio estimation. The presentation will also highlight the distinction between marginal and conditional estimands and discuss the pot ential role of prognostic risk scores or &ldquo\;supercovariates.&rdquo\;< br />
The webinar then moves to recent methodological developments be yond current standard practice. Sanne Roels (Johnson &\; Johnson) will explore extensions such as covariate adjustment in group sequential design s\, with particular attention to type I error control\, and discuss the mo ve toward data-adaptive approaches\, including pre-specified strategies su ch 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\, in cluding the use of data-adaptive and machine learning&ndash\;based estimat ors such as TMLE and related doubly robust methods. The talk will highligh t key challenges around interpretability\, pre-specification\, and regulat ory acceptability\, with particular attention to small sample settings and complex data structures such as clustered or multi-center trials. Practic al considerations for balancing innovation with robustness\, transparency\ , and trust in confirmatory analyses will also be discussed.

Th e session concludes with a panel discussion led by Jü\;rgen Hummel (Cy tel)\, bringing together regulatory\, industry\, and academic perspectives to reflect on current practice and future directions in covariate adjustm ent.

 \;

Registration

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

 \;

S peaker Details

Speaker

Biography

Ab stract

Dominic Magirr\, Novartis

Dominic is part of the Advanc ed Methodology and Data Science group at Novartis\, where he provides meth odological and technical support to clinical trial teams across a wide var iety of statistical topics. \;

In this presentation\, I will discuss well accepted methods fo r covariate adjustment\, including standardization (or g-computation) usin g generalized linear models\, as well as a covariate-adjusted version of t he log-rank test with a corresponding method for hazard ratio estimation. I will cover the distinction between marginal and conditional estimands an d discuss the potential role of risk scores or &ldquo\;supercovariates&rdq uo\;.

 \;

Sanne Roels\, Johnson &\; Johnson

 \;

Sanne is part of the statistical modelling\, methodology and consul ting group at J&\;J. The group supports teams through implementation of statistical innovation and impactful methodology\, leveraging modelling a nd simulation.

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

 \;

In this talk\, I will discuss methodological devel opments that go beyond what is currently generally accepted. I will discus s covariate adjustment in group sequential designs and related concerns ar ound type I error control. Next\, I will discuss the considerations of mov ing toward data‑adaptive methods\, including pre‑specified data‑adaptive s trategies such as TMLE across common endpoint types. \;

 \;

& nbsp\;

Kelly Van La ncker\, \;Ghent University and Vrije Universiteit Brussel

 \;

Kelly Van Lancker is an assistant professor in biostatistics at Ghent Universit y and Vrije Universiteit Brussels. She received both her master degree in mathematics and her PhD degree in Statistical Data Analysis from Ghent Uni versity. \; Previously\, Kelly was a postdoctoral researcher at the Jo hns Hopkins Bloomberg School of Public Health. Her goal is to develop inno vative designs and analytical techniques \;for drawing causal inferenc es in health sciences. A big part of her research focuses on more accurate and faster decision-making in randomized clinical trials by making optima l use of the available data. \;

 \;

This talk will discuss promising futur e directions and highlight key pitfalls and open problems. These include t he use of pre‑specified data‑adaptive and machine‑learning&ndash\;based es timators such as TMLE and related doubly robust methods. While these appro aches offer efficiency gains\, they raise practical challenges around inte rpretability\, pre‑specification\, and regulatory acceptability. Particula r attention will be paid to small‑sample settings\, where asymptotic guara ntees may be unreliable. The talk will also address clustered and correlat ed data structures\, common in multi‑center trials\, and their implication s for covariate adjustment. The session will conclude with practical consi derations on \;balancing methodological innovation with robustness\, t ransparency\, and trust in confirmatory analyses.

 \;

Jurgen Hummel\, Cytel

 \;

Jü\;rgen Hummel is Vice Pr esident\, Innovative Statistics at Cytel\, and in that role he provides st atistical consultancy to integrate advanced statistical approaches into de velopment programs. \; He has been working in Biostatistics in the CRO \, pharmaceutical and health care industry for more than 30 years in vario us project related\, technical and managerial positions. \; Prior to j oining Cytel\, Jü\;rgen led the Statistical Methodology groups at PPD (now Thermo Fisher Scientific) and at Novo Nordisk. \;

Jü\;rgen 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 ma thematics and economics at Augsburg University\, and he is a Chartered Sta tistician with the Royal Statistical Society.\n

 \;

Panel Discussion Lead


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