PSI Journal Club Webinar: Subgroup and Covariate Analysis

PSI Introduction to Industry Training (ITIT) Course - 2025/2026

An introductory course giving an overview of the pharmaceutical industry and the drug development process as a whole, aimed at those with 1-3 years' experience. It comprises of six 2-day sessions covering a range of topics including Research and Development, Toxicology, Data Management and the Role of a CRO, Clinical Trials, Reimbursement, and Marketing.

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.

Urgent Meeting: Medical Statistician Apprenticeship Scheme

The UK government have recently announced that level 7 apprenticeships must be fully funded by the employer from January 2026, for any apprentice over the age of 21. With funding for MSc's at an all time low, and universities courses facing closures, the apprenticeship scheme remains as important as ever, as a tool to encourage new statisticians into our industry. In this dedicated meeting, Valerie Millar (chair of the apprenticeship scheme) will provide full updates on the government changes and seek feedback and ideas from employers, universities and apprentices on how to keep this scheme successfully running for many years to come.

PSI Webinar: Methodology and first results of the iRISE (improving Reproducibility In SciencE) consortium

This 1-hour webinar will be an opportunity to hear about the methodology and first results of the iRISE consortium. iRISE is working towards a better understanding of reproducibility and the interventions that work to improve it. At the end of the presentation there will also be the opportunity to ask questions.

One-day Event: Change Management for Moving to R/Open-Source

This one-day event focuses on the comprehensive management of transitioning to R/Open-Source, addressing the challenges and providing actionable insights. Attendees will participate in sessions covering essential topics such as training best practices, creating strategic plans, making the case to senior management, and managing both statistical and programming aspects of the transition.

PSI Vaccines SIG Webinar: Challenges in Estimating Vaccine Effectiveness Against Progression to Severe Disease

This is a free webinar on Challenges in Estimating Vaccine Effectiveness Against Progression to Severe Disease.

PSI Book Club - The Art of Explanation: How to Communicate with Clarity and Confidence

Develop your non-technical skills by reading The Art of Explanation by Ros Atkins and joining the Sept-Dec 2025 book club. You will be invited to join facilitated discussions of the concepts and ideas and apply skills from the book in-between sessions.

PSI Training Course: Pediatric Extrapolation

This course is aimed at biostatisticians with no or some pediatric drug development experience who are interested to further their understanding. We will give you an introduction to the pediatric drug development landscape. This will include identifying the key regulations and processes governing pediatric development, a discussion on the needs and challenges when conducting pediatric research and a focus on the ways to overcome these challenges from a statistical perspective.

PSI Career Young Virtual Event (Q3 2025)

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

EFSPI/PSI Causal Inference SIG Webinar: Instrumental Variable Methods

The webinar is targeted at statisticians working in the pharmaceutical industry, and the objective is to 1) provide a basic understanding of IV methodology including how it relates to causal inference, and 2) present two inspirational pharma-relevant applications.

PSI Career Young Virtual Event (Q4 2025)

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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