PSI Journal Club Webinar: Subgroup and Covariate Analysis

PSI Book Club: Change: How organisations achieve hard-to-imagine results in uncertain and volatile times

Organizations have to adapt to the transforming landscape of our industry to ensure they continue to be successful in the future. Many of us are feeling the impact of organizational change. By reading John P Kotter’s book we can understand about organizational change and learn how to thrive, rather than just survive, through change. Change, by John P Kotter (and his team), is a summary of all that he has learned over his decades of research and leading change. His book describes why many current approaches to change are inadequate and explains why new solutions need to give people a voice and a role in a new, change-embracing organization. Develop your understanding of organisational change and become empowered to be part of your organisation’s change, by reading Change by John P Kotter and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply knowledge from the book in-between sessions.

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 Career Young Virtual Event (Q3 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Book Club: Another Door Opens – Book Club Special Event

This is a Book Club Special Event in response to the changes in our industry and as a supportive move to create community and connection for those navigating redundancy and uncertainty. Read the book in advance of the book club session then join the zoom call to discuss ideas. There will be breakout groups to connect with others, exchange experiences of how the book has helped, and offer support.

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.

PSI Training Course: Propensity Scores: Practical Application in Non-randomised Studies

The course will introduce the topic of propensity scores and the use of external data. Covering the topics of matching and weighting as well as more advance topics of high dimension propensity scores, multi-valued treatments, double robustness and time-varying scenarios. There will be the opportunity to participate in some hands on practical exercises in R.

Making Estimands Work - Practical Approaches with Implementation in Data Standards

This event will discuss the impact of the estimand framework on data flow and data handling for analysis datasets (ADaM). What are the considerations to produce data sets suitable for estimation of estimands and traceability of the occurrence of intercurrent events. This webinar will include tips for best practice, based on the White paper Implementation of ICH E9(R1) Estimands Framework using Data Standards.

PSI/EFSPI Biomarker SIG Webinar: Statistical Methods for ctDNA Data

This webinar will describe what ctDNA is and what the key challenges are for analysing it. We will then summarise and review methods that were published over the last few years for analysing ctDNA data.

PSI Training Course: Adaptive Designs

This two-afternoon virtual course provides a practical introduction to adaptive clinical trials, focusing on the concepts, applications, and regulatory principles outlined in ICH E20 through real-world examples and case studies.

PSI Careers - MEDMathS: Medicine Empowered by Data, Maths and Statistics

Date: Wednesday 4th November 2026 A careers talk about medical statistics and how it plays a crucial role in developing new medicines. Learn about the field of medical statistics and how it plays a crucial role in developing groundbreaking new medicines, vaccines and healthcare products.

PSI Medical Statistics Student Careers Event 2026

Date: 18 November 2026 This is an excellent opportunity for students to find out more about the field of medical statistics, talk to people from different organisations and make contacts for the future. All students currently studying for a mathematics or statistics-related BSc, MSc or PhD are invited to attend, and we welcome interest from exhibitors too.

PSI Career Young Virtual Event (Q4 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography
Björn Holzhauer	Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.
Thomas Jemielita	Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.
Chris Harbron	Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.

Speaker	Biography
Björn Holzhauer	Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.
Thomas Jemielita	Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.
Chris Harbron	Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.

Speaker	Biography
Björn Holzhauer	Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.
Thomas Jemielita	Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.
Chris Harbron	Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.

Speaker	Biography
Björn Holzhauer	Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.
Thomas Jemielita	Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.
Chris Harbron	Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.

Speaker	Biography
Björn Holzhauer	Björn holds a doctorate in mathematics from the Otto-von-Guericke University Magdeburg. He has helped develop drugs in several disease areas at Novartis for 20 years. Björn is one on the authors of a collection of case studies on applied flexible Bayesian modelling in drug development with brms. He has worked on exploring the opportunities for machine learning in clinical development.
Thomas Jemielita	Thomas Jemielita is a Principal Scientist in Oncology Statistics, BARDS. Since joining Merck in 2017, his evolving job roles have spanned across various areas, including statistical support for early to late phase studies, biomarker studies, competitive intelligence, strategic initiatives, and real-world evidence studies. He has been actively involved in statistical research and has authored/co-authored over 20 scientific publications in peer-reviewed statistical and clinical journals, along with currently being a member of the ASA BIOP RWE Scientific Working Group for Rare Diseases. His currently research interests include causal inference, machine learning, and software development. Prior to joining Merck, Thomas received his PhD in biostatistics from the University of Pennsylvania in 2017.
Chris Harbron	Chris Harbron is an Expert Statistician leading capabilities in Advanced Analytics within the Data Sciences function at Roche. Through a variety of roles within the pharmaceutical industry Chris has worked in all stages of the drug development pipeline from drug discovery to early and late development. Chris has published and presented widely both within the statistical and the broader scientific literature.

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