Here you can learn how to add new statistical methods to your toolbox. These may not be neccessarily cutting edge methods, but approaches, that you are not yet aware about.
As a statistician, you’re likely already familiar with SAS and the value it brings to data analysis. But in this ever-evolving world of statistics and data science, relying on your go-to language might not be enough to stay competitive anymore. The open-source language R is gaining traction within the healthcare industry as a powerful tool …
In our world of clinical trials and observational studies, missing data based on drop outs or for others reasons leads to a challenge in understanding how well treatments work. Treatment policy estimands help us to understand efficacy based on early treatment decisions. Various approaches, like reference-based imputation and delta adjustment, exist to speculate what may have …
In any industry, staying competitive and relevant requires a constant focus on innovation and the ability to communicate effectively with customers. In the pharmaceutical industry, this is particularly true as companies race to develop and market new compounds that can improve patient outcomes. In this episode, I share insights into the importance of effective data …
Your compound through the eyes of the competition Read More »
We statisticians play a significant role in the clinical development of pharmaceutical products and medical devices. Many of us focus on designing and analyzing clinical trials to provide evidence for regulatory approval and Health Technology Assessment (HTA). In these set-up, we often talk rather directly to our external clients – regulators and payers (at least …
Beyond Regulatory and HTA Success: Reaching Physicians and Patients Read More »
Interpretable machine learning (IML) is rapidly gaining popularity in the data science community. It offers a new way to build and interpret models that are more transparent and understandable. In this episode, we have the privilege of interviewing Serg Masis. Serg authored the book “Interpretable Machine Learning with Python: Learn to build interpretable high-performance models …
When it comes to clinical trial design, there are many challenges to overcome, including recruitment, protocol design, and data analysis, among others. Fortunately, simulations can provide a solution to some of those challenges. Simulations allow us to test the influence of assumptions, optimize designs, and understand probabilities of success before running the actual experiment. Together …
Simulations – your most powerful study design tool Read More »