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: Thursday 23rd October 2025\nTime: \;14:00-16:00 BST | 15:00-17:00 CET\nLocation: \;Online via Zoom\nSpeakers: Antonio Remir o-Azocar (Novo Nordisk)\, \;Benjamin Ackerman (Johnson &\; Johnson) \, Nerissa Nance (Novo Nordisk A/S) and Hana Lee\, FDA\n\nWho is this even t intended for?: Statisticians involved in or interested in evidence integ ration and causal inference.\n\nWhat is the benefit of attending?: Learn a bout recent developments in evidence integration and causal inference from key experts in academia and industry.\nCost\nThis webinar is free to both Members of PSI and Non-Members.\nRegistration\nTo register for this event \, please click here\nOverview\nIntegrating clinical trial evidence from c linical trial and real-world data is critical in marketing and post-author ization work. Causal inference methods and thinking can facilitate that wo rk in study design and analyses. In study planning\, causal inference thin king can clarify the target estimand and the fitness-for-purpose evaluatio n of each data source. In analysis\, methods can evaluate between source d ata heterogeneity and use efficient estimators.\n\nThis webinar will focus on causal inference/data fusion and illustrate their use with multiple ca se studies. Our first speaker\, Benjamin Ackerman\, will provide an overvi ew of terminology and various approaches for combining evidence from clini cal trials and real-world data. He will highlight key considerations for a ppropriate data selection\, draw connections between use-cases\, and intro duce relevant frameworks for study design and analysis. The second speaker \, Nerissa Nance will review research projects leveraging clinical trial a nd real-world data in full development. Our discussant (yet to be confirme d) will provide remarks on presented topics and a look forward.\nSpeaker d etails\n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Abstract\n \n \n \n  \;\n Antonio Remiro-Azocar\, Novo Nordisk\n Dr Antonio Re miro-Azó\;car is a statistical methodologist within the Methods and Outreach department in Novo Nordisk. His expertise lies in quantitative ev idence synthesis\, health technology assessment and data fusion. Prior to his current role\, Antonio was lead statistician for health technology ass essment at Bayer and an independent contractor providing statistical suppo rt to contract research organizations. Antonio holds a PhD in Statistical Science and an MSc in Machine Learning from University College London.\n  \;\n \n \n Benjamin Ackerman\,&nbs p\;Johnson &\; Johnson \n Benjamin Ackerman is a Principal S cientist at Johnson &\; Johnson\, where he provides support across ther apeutic areas to design and analyze randomized trials\, namely those that combine trial data with real-world data. He has expertise in causal infere nce methods to address various biases in both randomized trials and non-ex perimental studies\, namely to transport inferences from one population to another\, and to correct for outcome measurement error. Previously\, he w orked as a Quantitative Scientist at Flatiron Health\, an oncology real-wo rld data vendor\, where he oversaw the design of studies leveraging EHR da ta to improve cancer care in the United States. Ben holds a PhD in Biostat istics from the Johns Hopkins Bloomberg School of Public Health.\n \n &ldquo\;What is data fusion? Overview of approaches to i ntegrate evidence from multiple data sources&rdquo\;\n While ra ndomized trials remain the gold standard for estimating causal effects\, t here are often concerns that trial participants are not representative of broader target populations\, thereby impeding the generalizability (or ext ernal validity) of trial inferences. Non-experimental studies may be valua ble sources of evidence for drug effectiveness in routine care settings co vering broader patient populations\; however\, such data are subject to ma ny sources of bias that threaten internal validity and limit causal interp retations.\n Data fusion\, or the integration of information fr om multiple sources\, presents an opportunity to leverage strengths from b oth randomized and non-experimental studies and generate valid causal infe rences. This presentation will provide an overview of terminology and vari ous approaches to combine evidence from multiple sources\, namely clinical trials and real-world data. Key considerations for appropriate data selec tion will be discussed\, and relevant frameworks for study design and anal ysis will be introduced. \;\n \n \n \n Nerissa Nance\, Novo Nordisk A/S\n Nerissa Nance is a Lead Scientist at Novo Nordisk\, where she is part of a diverse scientific tea m that fosters strong academic collaborations with external institutions t o leverage new and innovative methodologies and generate strategic insight s. Much of this work is housed under the umbrella of the Joint Initiative for Causal Inference (JICI): a collaboration between Novo Nordisk and UC B erkeley\, Oxford\, UCL\, and others. The group&rsquo\;s work spans several causal inference-related methods relevant to trial analyses\, one of whic h is the integration of trial and real-world data to improve efficiency\, generalizability and feasibility. Nerissa holds a master&rsquo\;s and PhD in Biostatistics and Epidemiology\, respectively\, from UC Berkeley\; her dissertation focused on applications of longitudinal causal inference meth ods using targeted learning. \;\n  \;&ldquo\;Borrowed [ person] time: case studies of data fusion in the JICI collaboration&rdquo\ ;\n \n The traditional placebo-controlled cardiovasc ular outcome trial (CVOT) has been a historical cornerstone\, yet it can n o longer answer the relevant questions of the time. How can we begin to th ink outside the CVOT box? Collaborations between academia and industry hel p to ground ivory tower questions in concrete and relevant examples\, and simultaneous help to push industry leaders to demand more from their data. We will review a few relevant projects from the Novo-UC Berkeley and Novo -Oxford collaborations that begin to address how external data can be harn essed: methodologies\, key assumptions\, and advantages/limitations. \ ;\n \n \n  \;Hana Lee\, FDA\n Hana Lee\, PhD\, is a Senior Statistical Reviewer in the Office of Biostatisti cs (OB) at the Center for Drug Evaluation and Research (CDER)\, FDA. She l eads and oversees various FDA-led projects that support the development of the agency&rsquo\;s real-world evidence (RWE) program. She also serves as co-lead of the RWE Scientific Working Group of the American Statistical A ssociation (ASA) Biopharmaceutical Section\, a FDA public-private partners hip involving scientists from the FDA\, academia\, and industry to advance the understanding of real-world data (RWD) and RWE to support regulatory decision-making. In 2024\, she received the FDA&rsquo\;s most prestigious award for excellence in advancing and promoting statistical innovation in the use of RWD/RWE for regulatory decision-making.\n "Regulator y definitions of RWD/RWE: Why They Matter for Hybrid Design"\n \n \n\n DTEND:20251023T160000Z DTSTAMP:20260807T180705Z DTSTART:20251023T140000Z LOCATION: SEQUENCE:0 SUMMARY:Data Fusion\, Use of Causal Inference Methods for Integrated Inform ation from Multiple Sources UID:RFCALITEM639217228257540837 X-ALT-DESC;FMTTYPE=text/html:Date: Thursday 23rd October 2 025
\nTime: \;14:00-16:00 BST | 15:00-17:00 CET< br />\nLocation: \;Online via Zoom
\nSpe akers: Antonio Remiro-Azocar (Novo Nordisk)\, \; Benjamin Ackerman (Johnson &\; Johnson)\, Nerissa Nance (Novo Nordisk A/S) and Hana Lee\, FDA
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\nWho is this ev ent intended for?: Statisticians involved in or interested in evi dence integration and causal inference.
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\nWhat is the benefit of attending?: Learn about recent developments in eviden ce integration and causal inference from key experts in academia and indus try.
\n

Cost

\n

This webinar is free to both Members of PSI and Non-Members.

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Registration

\n

To register for this event \, please click here

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Overview

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Integrating clinical trial evidence from clinical trial and real-world data is critical in marketing and post-authorization work. Causal inference methods and thinking can facilitate that work in study de sign and analyses. In study planning\, causal inference thinking can clari fy the target estimand and the fitness-for-purpose evaluation of each data source. In analysis\, methods can evaluate between source data heterogene ity and use efficient estimators.
\n
\nThis webinar will focus o n causal inference/data fusion and illustrate their use with multiple case studies. Our first speaker\, Benjamin Ackerman\, will provide an overview of terminology and various approaches for combining evidence from clinica l trials and real-world data. He will highlight key considerations for app ropriate data selection\, draw connections between use-cases\, and introdu ce relevant frameworks for study design and analysis. The second speaker\, Nerissa Nance will review research projects leveraging clinical trial and real-world data in full development. Our discussant (yet to be confirmed) will provide remarks on presented topics and a look forward.

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Spe aker details

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&ldquo\;What is data fusion? Overv iew of approaches to integrate evidence from multiple data sources&rdquo\;

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While randomized trials remain the gold stan dard for estimating causal effects\, there are often concerns that trial p articipants are not representative of broader target populations\, thereby impeding the generalizability (or external validity) of trial inferences. Non-experimental studies may be valuable sources of evidence for drug eff ectiveness in routine care settings covering broader patient populations\; however\, such data are subject to many sources of bias that threaten int ernal validity and limit causal interpretations.

\n Data fus ion\, or the integration of information from multiple sources\, presents a n opportunity to leverage strengths from both randomized and non-experimen tal studies and generate valid causal inferences. This presentation will p rovide an overview of terminology and various approaches to combine eviden ce from multiple sources\, namely clinical trials and real-world data. Key considerations for appropriate data selection will be discussed\, and rel evant frameworks for study design and analysis will be introduced. \;\ n \n \n \n \n \n \n \n \n \n \n \n \n \n
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Speaker

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Biography

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Abstract

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 \;antonio-ra-pic
\n Antonio Remiro-Azocar\, Novo Nordisk		Dr Antonio Remiro-Az ó\;car is a statistical methodologist within the Methods and Outreac h department in Novo Nordisk. His expertise lies in quantitative evidence synthesis\, health technology assessment and data fusion. Prior to his cur rent role\, Antonio was lead statistician for health technology assessment at Bayer and an independent contractor providing statistical support to c ontract research organizations. Antonio holds a PhD in Statistical Science and an MSc in Machine Learning from University College London. \;
original-95259089- E54D-430B-A5A0-812AA1E8E979Benjamin Ackerman\, \;Johnson &\; Johnson Benjamin Ackerman is a Principal Scientist at Johnso n &\; Johnson\, where he provides support across therapeutic areas to d esign and analyze randomized trials\, namely those that combine trial data with real-world data. He has expertise in causal inference methods to add ress various biases in both randomized trials and non-experimental studies \, namely to transport inferences from one population to another\, and to correct for outcome measurement error. Previously\, he worked as a Quantit ative Scientist at Flatiron Health\, an oncology real-world data vendor\, where he oversaw the design of studies leveraging EHR data to improve canc er care in the United States. Ben holds a PhD in Biostatistics from the Jo hns Hopkins Bloomberg School of Public Health.
NerissaNerissa Nance\, Novo Nordisk A/SNerissa Nance is a Lead Scien tist at Novo Nordisk\, where she is part of a diverse scientific team that fosters strong academic collaborations with external institutions to leve rage new and innovative methodologies and generate strategic insights. Muc h of this work is housed under the umbrella of the Joint Initiative for Ca usal Inference (JICI): a collaboration between Novo Nordisk and UC Berkele y\, Oxford\, UCL\, and others. The group&rsquo\;s work spans several causa l inference-related methods relevant to trial analyses\, one of which is t he integration of trial and real-world data to improve efficiency\, genera lizability and feasibility. Nerissa holds a master&rsquo\;s and PhD in Bio statistics and Epidemiology\, respectively\, from UC Berkeley\; her disser tation focused on applications of longitudinal causal inference methods us ing targeted learning. \; \;&ldquo\;Borrowed [person] time: case studies of data fusion in the J ICI collaboration&rdquo\;
\n
\n The traditional placebo-controlled cardiovascular outcome trial (CVOT) has been a historical cornerstone\, yet it can no longer answer the relevant questions of the time. How can we begin to think outside the CVOT box? Col laborations between academia and industry help to ground ivory tower quest ions in concrete and relevant examples\, and simultaneous help to push ind ustry leaders to demand more from their data. We will review a few relevan t projects from the Novo-UC Berkeley and Novo-Oxford collaborations that b egin to address how external data can be harnessed: methodologies\, key as sumptions\, and advantages/limitations. \;
 \;Hana_LeeHana Lee\, FDAHana Lee\, PhD\, is a Senior Statistical Reviewer in the Office of Biostatistic s (OB) at the Center for Drug Evaluation and Research (CDER)\, FDA. She le ads and oversees various FDA-led projects that support the development of the agency&rsquo\;s real-world evidence (RWE) program. She also serves as co-lead of the RWE Scientific Working Group of the American Statistical As sociation (ASA) Biopharmaceutical Section\, a FDA public-private partnersh ip involving scientists from the FDA\, academia\, and industry to advance the understanding of real-world data (RWD) and RWE to support regulatory d ecision-making. In 2024\, she received the FDA&rsquo\;s most prestigious a ward for excellence in advancing and promoting statistical innovation in t he use of RWD/RWE for regulatory decision-making."Regulatory definitions of RWD/RWE: Why They Matter for Hybrid Design"
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