BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: \;Tuesday 19th November 2024\nTime: \;14:00-15:30 GMT | 15:00-16:30 CET\nLocation: \;Online via Zoom\nSpeakers: \;K aspar Rufibach\, Susan Gruber\, Florian Lasch\n\nWho is this event intende d for? \;Applied statisticians\, and people genuinely interested in ap plying state-of-the-art statistical methodology. \;\nWhat is the benef it of attending? \;Increased understanding and insights in causal infe rence principles and methodology. \;\nRegistration\nThis event is free to attend for both Members of PSI and Non-Members. To register your place \, please \;click here.\nOverview\nIn this webinar\, three speakers wi ll share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and o pportunities on why and how to apply causal inference principles / techniq ues in their work. And more importantly how causal approaches can help eva luating evidence for answers to causal-by-nature scientific questions.\nFi rst\, Kaspar Rufibach (Merck) will share his perspectives on opportunities to apply causal methods. Next\, Susan Gruber (TL revolution) will discuss targeted learning as a framework to address causal questions and the impo rtance of sensitivity analyses. Finally\, Florian Lasch (EMA) will discuss both the importance of the causal inference angle in determining estimand s\, and will discuss a case study.\nThe webinar will end with a panel disc ussion.\n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Abstract\n \n \n \n \n \n Kaspar Rufibach\n \n \n Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challenge the status quo \, with the aim of improving the drug development process\, making it more efficient\, and enabling access.\n Kaspar has co-founded and c o-leads the special interest group &ldquo\;Estimands in oncology&rdquo\; w hich has (as of August 2024) more than 100 members from 50 institutions gl obally. He has also co-founded and co-leads the EFSPI statistical methodol ogy leader group which has 14 members from 14 companies. He regularly inte racts with regulators globally on various joint projects.\n Kas par&rsquo\;s research interests are methods to optimize study designs\, pl atform trials\, advanced survival analysis\, probability of success\, esti mands and causal inference\, and estimation of treatment effects in subgro ups. Kaspar received training and worked as a statistician at the Universi ties of Bern\, Stanford\, and Zurich. From 2012 until 2024 he worked at Ro che before joining Merck KGaA in October 2024 to co-lead its Advanced Bios tatistical Science group.\n More on the oncology estimand WG: h ttp://www.oncoestimand.org\n More on the EFSPI statistical meth odology leaders group: https://efspieurope.github.io/efspi/methods/methods _intro.html\n More on Kaspar: http://www.kasparrufibach.ch\n \n \n I will start with providing a few exa mples of very valid scientific questions in drug development that typicall y ask for causal answers\, but which are routinely answered in ad-hoc ways that rarely allow for a causal interpretation. Further reasons why I beli eve a clinical biostatistician needs to know about causal inference will b e given. I will conclude with a call to apply and develop statistical and causal inference methodology to fill the gap between valid causal question s and routine ad-hoc answers.\n  \;\n  \;\n \n \n \n \n \n S usan Gruber\n \n \n Susan Gruber\, co-fou nder of TL Revolution and Founder of Putnam Data Sciences\, is a biostatis tician and computer scientist specializing in  \;causal inference and predictive modeling.  \; Her work focuses on improving methods and too ls for generating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her \;tmle  \;R package on CRAN has over 70\,000 downloads worldwide. \;\n \n \n Targeted Learning is a framework that comb ines causal inference\, statistics\, and machine learning to address compl ex issues in analyzing data from randomized controlled trials and studies that incorporate real-world data. This talk provides a high-level introduc tion to the Targeted Learning Estimation Roadmap\, statistical analysis us ing Targeted Maximum Likelihood Estimation (TMLE)\, and the role of sensit ivity analysis to assess the level of support for drawing a substantive co nclusion from the study findings.\n \n \n \n \n \n Florian Lasch\n \n \n \n Florian is a Biostatistician with a degree i n mathematics and a PhD from Hannover Medical School. Florian works as a B iostatistics Specialist at the European Medicines Agency\, providing scien tific support to development and evaluation throughout all stages of marke ting authorisation assessments of medicinal products\, and leads the ACT E U Priority Action on Clinical Trial Methodologies and the EMA Estimands Im plementation Group.\n \n \n The estimands framework facilitates the application of thinking and methodology develop ed in the causal inference community to the design and analysis of clinica l trials. This presentation will reflect on the opportunities and challeng es of applying causal inference methodology to clinical trials. A case stu dy in Alzheimer&rsquo\;s Disease where the intercurrent event &lsquo\;init iation of symptomatic medication&rsquo\; is handled with a hypothetical st rategy will illustrate the key points. \; \;\n \n \n \n\n \;\n \; DTEND:20241119T150000Z DTSTAMP:20260511T000605Z DTSTART:20241119T140000Z LOCATION: SEQUENCE:0 SUMMARY:Joint PSI/EFSPI Causal Inference SIG Webinar: Opportunities in appl ying a causal inference framework during the analysis of an RCT UID:RFCALITEM639140547655855589 X-ALT-DESC;FMTTYPE=text/html:Date: \;Tuesday 19th Nove mber 2024
\nTime: \;14:00-15:30 GMT | 15:00-16:3 0 CET
\nLocation: \;Online via Zoom
\nSpeakers: \;Kaspar Rufibach\, Susan Gruber\, Florian Lasch
\n
\nWho is this event intended for? \;Applied statisticians\, and people genuinely interested in applying state-of-the-art statistical methodology. \;
\nWhat is th e benefit of attending? \;Increased understanding and insight s in causal inference principles and methodology. \;
\n

Regist ration

\n

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

\n

Overview

\n

In this webinar\, three speakers will share their perspective on the using of causal inference methodology in the analysis of RCT data. The audience will be presented with ideas and opportunities o n why and how to apply causal inference principles / techniques in their w ork. And more importantly how causal approaches can help evaluating eviden ce for answers to causal-by-nature scientific questions.

\n

First\, K aspar Rufibach (Merck) will share his perspectives on opportunities to app ly causal methods. Next\, Susan Gruber (TL revolution) will discuss target ed learning as a framework to address causal questions and the importance of sensitivity analyses. Finally\, Florian Lasch (EMA) will discuss both t he importance of the causal inference angle in determining estimands\, and will discuss a case study.

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The webinar will end with a panel disc ussion.

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The estimands framework facilitates the a pplication of thinking and methodology developed in the causal inference c ommunity to the design and analysis of clinical trials. This presentation will reflect on the opportunities and challenges of applying causal infere nce methodology to clinical trials. A case study in Alzheimer&rsquo\;s Dis ease where the intercurrent event &lsquo\;initiation of symptomatic medica tion&rsquo\; is handled with a hypothetical strategy will illustrate the k ey points. \; \;

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Speaker

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Biography

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Abstract

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kaspar1 (002)
\n Kaspar Rufibach

\n 		\n

Kaspar Rufibach is a biostatistician who is passionate about supporting statisticians and drug developers to continuously challe nge the status quo\, with the aim of improving the drug development proces s\, making it more efficient\, and enabling access.
\n Kas par has co-founded and co-leads the special interest group &ldquo\;Estiman ds in oncology&rdquo\; which has (as of August 2024) more than 100 members from 50 institutions globally. He has also co-founded and co-leads the EF SPI statistical methodology leader group which has 14 members from 14 comp anies. He regularly interacts with regulators globally on various joint pr ojects.
\n Kaspar&rsquo\;s research interests are methods to optimize study designs\, platform trials\, advanced survival analysis\, probability of success\, estimands and causal inference\, and estimation of treatment effects in subgroups. Kaspar received training and worked as a statistician at the Universities of Bern\, Stanford\, and Zurich. From 2 012 until 2024 he worked at Roche before joining Merck KGaA in October 202 4 to co-lead its Advanced Biostatistical Science group.

\n < p>More on the oncology estimand WG: http://www.oncoestimand.org

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More on the EFSPI sta tistical methodology leaders group: https://efspieurope.github.io/efspi/met hods/methods_intro.html

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More on Kaspar: http://www.kasparrufibach.ch

\n 		\n

I will start wi th providing a few examples of very valid scientific questions in drug dev elopment that typically ask for causal answers\, but which are routinely a nswered in ad-hoc ways that rarely allow for a causal interpretation. Furt her reasons why I believe a clinical biostatistician needs to know about c ausal inference will be given. I will conclude with a call to apply and de velop statistical and causal inference methodology to fill the gap between valid causal questions and routine ad-hoc answers.

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sgruber
\n Susan Gruber

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Susan Gruber\, co-founder of TL R evolution and Founder of Putnam Data Sciences\, is a biostatistician and c omputer scientist specializing in  \;causal inference and predictive m odeling.  \; Her work focuses on improving methods and tools for gener ating robust real-world evidence to support biopharmaceutical and medical decision-making through Targeted Learning. Her \;tmle \;R package on CRAN has over 70\,000 downloads worldwide. \;

\n 		\n

Targeted Learnin g is a framework that combines causal inference\, statistics\, and machine learning to address complex issues in analyzing data from randomized cont rolled trials and studies that incorporate real-world data. This talk prov ides a high-level introduction to the Targeted Learning Estimation Roadmap \, statistical analysis using Targeted Maximum Likelihood Estimation (TMLE )\, and the role of sensitivity analysis to assess the level of support fo r drawing a substantive conclusion from the study findings.

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foto Florian Lasch
\n Florian Lasch
\n

\n 		\n

Florian is a Biostatisticia n with a degree in mathematics and a PhD from Hannover Medical School. Flo rian works as a Biostatistics Specialist at the European Medicines Agency\ , providing scientific support to development and evaluation throughout al l stages of marketing authorisation assessments of medicinal products\, an d leads the ACT EU Priority Action on Clinical Trial Methodologies and the EMA Estimands Implementation Group.

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