Yves and I have worked together in various roles. Over time, he became more interested in becoming a statistician and I helped him to change his career path in this direction.
As part of his master thesis, he worked on an idea; I had sketched out a couple of years ago but never had the time to fully think through. I wanted to explore how we could utilize existing study data to inform the benefit-risk assessment of different therapies. In this episode, you will learn a new concept which also is related to minimal clinical meaningful differences and helps to assess the impact of various adverse events on the patient.
Specifically, we dive into:
- What is the benefit-risk tolerability measure?
- How can we use the information on which patient discontinue to inform the benefit-risk assessment?
- How does the model help us rank adverse events in order of their importance?
- Which adverse events cannot be classified with the model?
- How to use the model to inform relevant difference for efficacy endpoints?
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Dr. Yves Schymura
Associate Consultant Statistician at Lilly Deutschland GmbH
He studied Physiological Sciences and Pharmacology at the University of Oxford before pursuing his doctoral studies at the Max-Planck-Institute for Heart and Lung Research. For the past 7 years he worked in the German HTA context with increasing roles of responsibility, at last as HTA Team Lead / Early Pipeline Team Lead Immunology at AbbVie. During that time he discovered his passion for data and how to best use it to enable insight, effectively communicate results and generate value.
In 2019 the finished his extra-occupational study of Medical Biometry / Biostatistics at the University of Heidelberg, his master’s thesis dealing with a novel method on how to judge the patient relevance of adverse events using data from clinical trials to inform benefit-risk assessments. Since then he is working as a statistician in the international business unit of Lilly in the areas of medical affairs and HTA. In his spare time he enjoys spending time with his family, in the outdoors and is an avid cyclist.
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This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
