PSI Sub Group SIG Webinar: Modern approaches to subgroup identification

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.

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
David Svensson, SIG lead (AstraZeneca)	Senior Statistical Science Director at the Statistical Innovation group, Data Science and AI, Gothenburg. Has a Ph.D. in Mathematical Statistics from Chalmers University of Technology, Sweden and was Post Doc at the University of Zurich & ETH in 2000-2003, working & teaching in the Bioinformatics field. Joined AstraZeneca R&D in 2003, with involvement ranging from Discovery science to Late Phase drug development, with a particular interest in exploratory statistical modelling and machine learning. Current Lead of the EFSPI Subgroup Special Interest Group.	Introduction Introducing the SIG and discussing the taxonomy of the topics falling under the SIG umbrella. A short overview of the various flavors of subgroup analyses in pharmaceutical drug development e.g., confirmatory testing, regulatory Consistency assessment, subgroup selection and detection using Causal Inference Machine Learning (ITE/ITR approaches).
Ilya Lipkovich (Eli Lilly)	Ilya Lipkovich is a Sr. Research Advisor at Eli Lilly and Company. Ilya received his PhD in Statistics from Virginia Tech in 2002 and has more than 15 years of statistical consulting experience in pharmaceutical industry. He is an ASA Fellow and published on subgroup identification in clinical data, analysis with missing data, and causal inference. He is a frequent presenter at conferences, a co-developer of subgroup identification methods, and a co-author of the books “Analyzing Longitudinal Clinical Trial Data. A Practical Guide” and “Estimands, Estimators and Sensitivity Analysis in Clinical Trials.”	Overview of recent innovations in subgroup identification for personalized medicine and related methods Ilya Lipkovich (Eli Lilly) and Alex Dmitrienko (Mediana) In this talk we review recent advances in statistical methods for personalized medicine including subgroup identification (SI) and estimation of individualized treatment regimens (ITR) from randomized clinical trials and observational data. We identify several types of approaches using features introduced in Lipkovich, Dmitrienko and D’Agostino (2017) that distinguish principled methods from various simplistic approaches to data-driven subgroup identification and discuss common measures for evaluating performance of SI and ITR.
Björn Bornkamp (Novartis)	Björn Bornkamp works since 10 years in the Statistical Methodology Group at Novartis in Basel, where he provides consulting to statisticians and clinical teams on topics related to dose-finding studies, subgroup analyses, Bayesian statistics as well as estimands and causal inference. Björn holds a PhD in Statistics from TU Dortmund University in Germany. He received the RSS/PSI award for developing statistical dose-finding methodology, in particular the DoseFinding R package	Benchmarking algorithms for discovering subgroups with differential treatment effect The identification and estimation of heterogeneous treatment effects in biomedical clinical trials is challenging because trials are typically planned to demonstrate an effect in the overall trial population. Nevertheless, the identification of how the treatment effect may vary across subgroups is of major importance for drug development. In this work, we review and benchmark recent methods for identifying and estimating the heterogeneous treatments effects using various metrics and scenarios relevant for drug development. Our focus is not only on a comparison of the methods in general, but on how well these methods perform in realistic simulation scenarios.
Kostas Sechidis (Novartis)	Kostas is an Associate Director of Data Science in Novartis’ Advanced Exploratory Analytics group. His main areas of interest are machine learning based biomarker discovery, subgroup identification, and development of digital endpoints. Kostas did his PhD in statistical machine learning on the area of hypothesis testing and feature selection in semi-supervised scenarios in the University of Manchester’s Department of Computer Science. Afterwards, he spent many years as post-doctoral researcher on developing novel methodologies for analysing: self-reported epidemiological data with Manchester’s Health e-Research Center, clinical trials data for personalised medicine with AstraZeneca and digital healthcare data for digital biomarker development with Roche. More information about his work can be found at: https://sechidis.netlify.app/	Using knockoffs for controlled predictive biomarker identification One of the key challenges of personalized medicine is to identify which patients will respond positively to a given treatment. The first step towards this is to identify the baseline variables (e.g. biomarkers) that influence the treatment effect, which are known as predictive biomarkers. When we discover predictive biomarkers it is crucial to have control over the false-positives to avoid waste of resources, as well as provide guarantees over the replicability of our findings. With our work we introduce a set of methods for controlled predictive biomarker discovery, and we use them to explore heterogeneity in psoriatic arthritis trials.
Paolo Eusebi (UCB Pharma)	Eusebi is a Statistician and Statistical Programmer. After obtaining a PhD in Mathematics and Statistics in 2007, he has worked as a statistician both in academia and in public health sector. Since 2019 he joined UCB and the pharmaceutical industry. His main research interests are evidence synthesis, patient reported outcomes, statistical learning techniques for subgroup identification, and data visualization. He is Senior Editorial Board Member of BMC Medical Research Methodology and an active member of PSI and Data Visualization Society.	Visualize and interact with subgroups Graphical approaches are routinely employed in subgroup analysis, typically for describing treatment effect sizes of subgroups. Such graphical displays could be either static or interactive, escriptive or exploratory. Such visualization techniques could be handsome in summarizing a pre-specified analysis, or they could serve as an exploratory tool. One example of an interactive exploratory tool is the subscreen R package. A mini review of such graphical approaches to subgroup problems is presented. Furthermore, we will introduce a novel tool for the exploration of predictive factors of safety and efficacy in a pool of clinical trials.

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