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What strategies do you use in your work as a statistician to ensure successful outcomes? How do you set expectations with stakeholders when tackling a project?Are there any common mistakes statisticians make when setting expectations in their job that you have learned to avoid?How setting realistic expectations can positively impact
What knowledge gaps exist regarding the implementation of these new analytical techniques, and how can statisticians and data scientists bridge them to maximize effectiveness of clinical research? Despite the promises of Bayesian approaches, the lack of expert training, software tools, and computational resources has made their adoption slow. Statisticians and
As statisticians, we rely on good quality from programmers and data management. And if you talk to your colleagues, everybody has a story about a poor experience. But the questions are: How do we collaborate with other departments to create a quality deliverables?How to strike the right balance between working
For years, creating data visualizations has frustrated statisticians, data analysts and data scientists. Software – especially SAS – hasn’t been great and organisation have tried to take the pain out of the process by creating standard figures. But these standard graphics fail to communicate the message to the intended audience
Have you been considering a one-armed observational study or asked to work on such a study? As a statistician, it is important to understand the scientific but even more the political background of such studies. In this episode, we will discuss one-armed observational studies and why they are, in most
The world of healthcare continues to change, and it’s important to keep up with the latest advances in technology and research. That’s why I’m excited to talk with Shirley Wang, one of the leaders of RCT Duplicate, a study focused on duplicating randomized clinical trials through real world data. She is currently leading
Today I’m talking with Rachel Tham, a principal statistician, about a book that has been incredibly impactful in both of our lives—Deep Work. We discuss what deep work is and how it can help statisticians at work. Deep work is a concept developed by Cal Newport which encourages people to
Why are companies often running single arm studies in development? What are the potential drawbacks and why is it not a binary choice between full comparative study and single arm trial? What are solutions in between to find a balance between feasibility and rigour? Single arm studies are a popular
Reproducible research is a key part of research in the pharma industry. It allows for transparency, understanding, and accuracy in the research process. But how can you make your research more reproducible? Today, I talk with Heidi Seibold who has dedicated her career to helping researchers become more reproducible. Let’s
Are you a statistician working in the pharmaceutical industry and never got in touch with economic modelling and network meta-analyses? Then you should listen to this episode! The EU HTA will not only affect all statisticians in the pharmaceutical industry with respect to skill sets and collaboration (we talked about
All statisticians in the Pharmaceutical Industry will be impacted by the new EU HTA regulation. Activities around HTA submissions will happen earlier than currently, in parallel to the regulatory approval process for marketing authorization. There will be an increased scope of evidence for the joint clinical assessment to fulfill the
End of last year, a congressional report about the approval of an Alzheimer drug by the FDA was published. It critiques both the FDA and the sponsor company Biogen. Reuters and many other major news organisations reported about it. Today, I interviewed Anja Schiel about this topic. We talked about
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