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9:00 - 12:00 (3h)
SHORT course: Applied Modelling in Drug Development - Flexible regression modelling in Stan via brms
Sebastian Weber, Lukas Widmer (Novartis)
14:00 - 17:00 (3h)
Plenary 1: Modern clinical trials: the challenges of small populations and novel endpoints
14:00 - 15:00 (1h)
Advances in statistical methods for clinical trials in rare diseases: an overview
Nigel Stallard (Warwick Medical School)
15:00 - 16:00 (1h)
Eight Methodological Questions for Digital Outcome Measures
Mia Tackney (MRC, University of Cambridge)
16:00 - 17:00 (1h)
Contributed speakers
17:00 - 20:00 (3h)
Poster & wine session
Poster & wine session
9:00 - 12:00 (3h)
Plenary 2: Use of external information to improve decision making I
9:00 - 10:00 (1h)
The Good, the Bad and the Ugly: Experiences When Utilizing External Information in Clinical Trials
Simon Wandel (Novartis)
10:00 - 11:00 (1h)
Regulatory view on use of external information: Considerations from design to assessment
Kit Roes (Radboud University Medical Center Nijmegen)
11:00 - 12:00 (1h)
Contributed speakers
14:00 - 17:00 (3h)
Plenary 3: Improving statisticians‘ communication and coding skills
14:00 - 15:00 (1h)
Why we Need to Improve Software Engineering in Biostatistics - A Call to Action
Daniel Sabanés Bové (RCONIS)
15:00 - 16:00 (1h)
Data Insights: Bridging the Gap Between Numbers and Knowledge
Fanny Chevalier (University of Toronto)
16:00 - 17:00 (1h)
Contributed speakers
19:30 - 22:30 (3h)
Conference dinner
Conference dinner
9:00 - 12:00 (3h)
Plenary 4: Use of external information to improve decision making II
9:00 - 10:00 (1h)
Covariate-adjusted Robust Mixture Prior approach in clinical trials with historical controls
Andrea Callegaro (GSK)
10:00 - 11:00 (1h)
Health Techology Assessment view on use of external information
Beate Wieseler (IQWiG)
11:00 - 12:00 (1h)
Contributed speakers
14:00 - 17:00 (3h)
SHORT COURSE: ): Good Software Engineering Practice for R Packages - Daniel Sabanés Bové (RCONIS) & Jack Talboys (Novartis)
Join us for an engaging half-day face-to-face short-course designed to enhance your R programming skills with a focus on developing reliable R packages used in statistics or data science. This course is a blend of informative presentations and hands-on coding parts, aimed at equipping participants with practical tools and techniques for engineering high-quality R packages. This course is tailored for individuals who are already comfortable with writing functions in R and are looking to elevate their package development skills. Bring your laptop and be prepared to transform your approach to R package development through hands-on learning and collaboration. Whether you're looking to improve your workflow, meet regulatory standards, or simply enhance the quality of your statistical tools, this course offers valuable insights and skills to achieve your goals.
