Networking Event
Welcome to the fifth biostatistics network meeting on Thursday 15 May, 2025, at Karolinska Institutet’s Solna campus. The goal of this meeting is to encourage biostatisticians and others working in related fields to meet and discuss topics of mutual interest, with this years theme focusing on: Applying Bayesian Statistics in Medical Research.
The event is free and open to everyone working in, or interested in, biostatistics, bioinformatics, data science, epidemiology, and related fields. We welcome colleagues from all branches (academia/industry/government) and geographic locations.
Karolinska Institutet Event Page: https://news.ki.se/calendar/biostatistics-networking-event-applying-bayesian-statistics-in-medical-research
To register for the event, please fill out this short online form.
Program
Section titled “Program”The program consists of a scientific part with 3 invited keynote speakers and a panel discussion. The scientific presentations will be followed by an informal networking session.
| Time | Topic |
|---|---|
| Welcome and introduction | |
| Professor emeritus Marcel Zwahlen, PhD, University of Bern, Switzerland Probability: A somewhat mysterious mathematical object at the heart of “Bayesian statistics” | |
| Professor Rhiannon Owen, Swansea University Medical School, UK Title to be confirmed | |
| Short break | |
| Dr Michael Crowther, Red Door Analytics, Stockholm, Sweden Title to be confirmed | |
| Panel discussion (moderated by Simon Steiger) | |
| Closing of scientific part | |
| Mingle with food and drinks (Foyer, Aula Medica) |
Speakers
Section titled “Speakers”Marcel Zwahlen is professor emeritus of epidemiology and biostatistics at the institute of social and preventive medicine (ISPM) at the University of Bern with a first degree in theoretical physics from the University of Bern and a PhD in epidemiology from John Hopkins University, Baltimore, USA. Before joining ISPM in 2003, he was head of the scientific office at the Swiss Cancer League (a charity organization) and before that head of the section of viral diseases at the Swiss Federal Office of Public Health in Bern (government office). He is a methodologist with a long-standing interest and experience in the analysis of health related observational and longitudinal data. He promotes the use of probabilistic and – ideally - deterministic record linkage methods to enrich existing data, and the use of Bayesian / fully probabilistic approaches for extracting or discussing the available information from existing data (hopefully of good quality).
Rhiannon Owen is Professor of Statistics at Swansea University Medical School. Her main research interests include the development and application of Bayesian methods in Health Technology Assessment, Population Health, and Health Service Evaluation. In particular, her research interests include evidence synthesis methods, analysis of large scale linked electronic health records, simulation-based methods, clinical trial evaluation, economic decision modelling, and value of information.
Michael Crowther obtained his PhD in medical statistics at the University of Leicester where he also spent many years an academic biostatistician, rising to Associate Professor of Biostatistics, before relocating to Stockholm in 2021 where he founded Red Door Analytics. He is an expert in survival analysis and joint longitudinal-survival models, having made numerous contributions to the fields, and widely respected as a statistical software developer. He has developed and taught many training courses on his research, and is a Fellow of the UK Higher Education Academy.
Abstracts
Section titled “Abstracts”Probability: A somewhat mysterious mathematical object at the heart of “Bayesian statistics”
I will cover fundamental aspects of “probability”, discuss whether it is a concept, a mathematical object, or part of logical reasoning. I will present examples to illustrate how and why I arrived at looking very carefully at the Bayesian/fully probabilistic way of extracting information from data. Finally, I will propose to replace the terminology “Bayesian statistics” by “fully probabilistic approach”.