When I started my career as a statistician in the clinical world, I was wondering about safety analysis. I thought it’s always the same – count the patients with an event – job done! Then just repeat.
But it’s not that simple, especially in more complex situation where you have different follow-up times. Then the patients that stay longer on treatment as they benefit from it, also get more adverse events. Naively counting the patients with events or the number of events may make the beneficial treatment look worse. So, how can you account for this?
Join us while we dive deep into the following points:
- How do you solve the problem about varying follow-up times?
- What’s so special about the setup of the SAVVY collaboration?
- Where can people learn more about SAVVY?
References:
- Stegherr et al (2021) Biom J https://doi.org/10.1002/bimj.201900347
- Preprint https://arxiv.org/abs/2001.05709
- One-sample case: https://arxiv.org/abs/2008.07883
- Two-sample case: https://arxiv.org/abs/2008.07881
- Example R code (markdown file): https://numbersman77.github.io/AEprobs/SAVVY_AEprobs.html
- “Special Issue:Analysis of Adverse Event Data”
- Van Walraven
- see also Letter
- Related paper on: “On estimands and the analysis of adverse events in the presence of varying follow-up times within the benefit assessment of therapies”
- Working Savvy
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Kaspar Rufibach
EXPERT STATISTICAL SCIENTIST AT ROCHE BIOSTATISTICS
He is an Expert Statistical Scientist in Roche’s Methods, Collaboration, and Outreach group and located in Basel.
He does methodological research, provides consulting to Roche statisticians and broader project teams, gives biostatistics trainings for statisticians and non-statisticians in- and externally, mentors students, and interacts with external partners in industry, regulatory agencies, and the academic community in various working groups and collaborations.
He has co-founded and co-leads the European special interest group “Estimands in oncology” (sponsored by PSI and EFSPI, which also has the status as an ASA scientific working group, a subsection of the ASA biopharmaceutical section) that currently has 39 members representing 23 companies, 3 continents, and several Health Authorities. The group works on various topics around estimands in oncology.
Kaspar’s research interests are methods to optimize study designs, advanced survival analysis, probability of success, estimands and causal inference, estimation of treatment effects in subgroups, and general nonparametric statistics. Before joining Roche, Kaspar received training and worked as a statistician at the Universities of Bern, Stanford, and Zurich.
More on the oncology estimand WG: http://www.oncoestimand.org
More on Kaspar: http://www.kasparrufibach.ch
Dr. Jan Beyersmann
Research Interest
- Survival and Event History Analysis
- Statistical Methodology for Clinical and Epidemiological Studies
Brief CV
- 2013- Professor of Biostatistics, University Ulm
- 2012- Habilitation for ‘Medical Biometry and Statistics’, Medical Faculty, University of Freiburg
- 2005- Graduation Dr. rer. nat., Faculty of Mathematics and Physics, University of Freiburg
- 2001 – 2012 Scientist at the Institute of Medical Biometry and Medical Informatics, University Hospital Freiburg
- 2000-2001 Biometrician at Beiersdorf Research Centre, Hamburg
- 1999 Diploma in Mathematics, University of Duesseldorf
More on Dr. Jan: https://www.uni-ulm.de/mawi/statistics/team/professors/prof-dr-jan-beyersmann/
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