Data Fusion, Use of Causal Inference Methods for Integrated Information from Multiple Sources

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

Topic: R Package Basics. 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, “R Package Basics,” will introduce the fundamentals of working with R packages—covering how to install, load, and manage them effectively to support data analysis and reproducible research. The session will provide a solid starting point, clarify common misconceptions, and offer valuable resources for continued learning.

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

PSI Book Club Lunch and Learn: Communicating with Clarity and Confidence

If you have read Ros Atkins’ book The Art of Explanation or want to listen to the BBC’s ‘Communicator in Chief’, you are invited to join the PSI Book Club Lunch and Learn, to discuss the content and application with the author, Ros Atkins. Having written the book within the context of the news industry, Ros is keen to hear how we have applied the ideas as statisticians within drug development and clinical trials. There will be dedicated time during the webinar to ASK THE AUTHOR any questions – don’t miss out on this exclusive PSI Book Club event! Haven’t read the book yet? Pick up a copy today and join us. Explanation - identifying and communicating what we want to say - is described as an art, in the title of his book. However, the creativity comes from Ros’ discernment in identifying and describing a clear step-by-step process to follow and practice. Readers can learn Ros’ rules, developed and polished throughout his career as a journalist, to help communicate complex written or spoken information clearly.

PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities

This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation. The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication. The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication. The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.

Speaker	Biography	Abstract
Antonio Remiro-Azocar, Novo Nordisk	Dr Antonio Remiro-Azócar is a statistical methodologist within the Methods and Outreach department in Novo Nordisk. His expertise lies in quantitative evidence synthesis, health technology assessment and data fusion. Prior to his current role, Antonio was lead statistician for health technology assessment at Bayer and an independent contractor providing statistical support to contract research organizations. Antonio holds a PhD in Statistical Science and an MSc in Machine Learning from University College London.
Benjamin Ackerman, Johnson & Johnson	Benjamin Ackerman is a Principal Scientist at Johnson & Johnson, where he provides support across therapeutic areas to design and analyze randomized trials, namely those that combine trial data with real-world data. He has expertise in causal inference methods to address 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 Quantitative Scientist at Flatiron Health, an oncology real-world data vendor, where he oversaw the design of studies leveraging EHR data to improve cancer care in the United States. Ben holds a PhD in Biostatistics from the Johns Hopkins Bloomberg School of Public Health.	“What is data fusion? Overview of approaches to integrate evidence from multiple data sources” While randomized trials remain the gold standard for estimating causal effects, there are often concerns that trial participants 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 effectiveness in routine care settings covering broader patient populations; however, such data are subject to many sources of bias that threaten internal validity and limit causal interpretations. Data fusion, or the integration of information from multiple sources, presents an opportunity to leverage strengths from both randomized and non-experimental studies and generate valid causal inferences. This presentation will provide an overview of terminology and various approaches to combine evidence from multiple sources, namely clinical trials and real-world data. Key considerations for appropriate data selection will be discussed, and relevant frameworks for study design and analysis will be introduced.
Nerissa Nance, Novo Nordisk A/S	Nerissa Nance is a Lead Scientist at Novo Nordisk, where she is part of a diverse scientific team that fosters strong academic collaborations with external institutions to leverage new and innovative methodologies and generate strategic insights. Much of this work is housed under the umbrella of the Joint Initiative for Causal Inference (JICI): a collaboration between Novo Nordisk and UC Berkeley, Oxford, UCL, and others. The group’s work spans several causal inference-related methods relevant to trial analyses, one of which is the integration of trial and real-world data to improve efficiency, generalizability and feasibility. Nerissa holds a master’s and PhD in Biostatistics and Epidemiology, respectively, from UC Berkeley; her dissertation focused on applications of longitudinal causal inference methods using targeted learning.	“Borrowed [person] time: case studies of data fusion in the JICI collaboration” 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? Collaborations between academia and industry help 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 harnessed: methodologies, key assumptions, and advantages/limitations.
Hana Lee, FDA	Hana Lee, PhD, is a Senior Statistical Reviewer in the Office of Biostatistics (OB) at the Center for Drug Evaluation and Research (CDER), FDA. She leads and oversees various FDA-led projects that support the development of the agency’s real-world evidence (RWE) program. She also serves as co-lead of the RWE Scientific Working Group of the American Statistical Association (ASA) Biopharmaceutical Section, a FDA public-private partnership 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’s most prestigious award for excellence in advancing and promoting statistical innovation in the use of RWD/RWE for regulatory decision-making.	"Regulatory definitions of RWD/RWE: Why They Matter for Hybrid Design"

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