New Data Collaboration between UCPH, Novo Nordisk and UC Berkeley
The Department of Public Health at UCPH has entered into a collaboration with leading researchers in machine learning and biostatistics at UC Berkeley in California. The DKK 25 million collaboration seeks to extract more knowledge from drug trials and real world data on the causal relationships between different diseases and medicinal products.
Looking at disease and health from a statistical perspective will reveal a lot of connections at population level, just as huge amounts of data are collected via medical trials. But it is often really difficult to say anything about cause and effect, not least with regard to the effect of treatments outside the controlled test environment.
A new collaboration between the Department of Public Health at the University of Copenhagen, UC Berkeley and Novo Nordisk aims to change that. The collaboration, which is called the Joint Initiative for Causal Inference, focusses on identifying causal connections.
‘Our colleagues at UC Berkeley are world-leading when it comes to research at the intersection between statistical methods, machine learning and causal inference methods. Combining that with our experience with register-based research and Novo Nordisk’s highly specialised competences within drug trials will really help propel the area forward. We are therefore very enthusiastic about this opportunity’, says Associate Professor and Deputy Head of Department Theis Lange from the Department of Public Health.
Data Mine of Information
Among other things, the collaboration will look into whether Danish register data can be combined with data from drug trials. Such analyses require combining classical biostatistics with machine learning.
The UCPH part of the project will be headed by Theis Lange and Professors Torben Martinussen and Thomas Gerds, all from the Section of Biostatistics, and it will involve both postdocs and PhD students.
The collaboration was launched at a Webinar at the end of October, introducing the new methods to an audience of both data researchers and clinicians. Watch the presentations here
Associate Professor in Biostatistics Theis Lange
+45 29 93 63 75