statistics

While most of our day-to-day activities consist of running clinical studies, submissions, and directly related work, we also engage in such innovation work streams or process improvement working groups and other such activities.

As statisticians in the medical field, we’re used to studying subgroups of patients with respect to all kinds of biological variables: from demographics to genomics. This provides us with a good understanding of what the benefit-risk profile for a given patient looks like.

In todays episode, we speak with Michael O’Kelly, an expert on this topic with lots of presentations around estimand (see e.g. the PSI events). He also won the award for Statistical Excellence in the Pharmaceutical Industry, jointly run by the RSS and Statisticians in the Pharmaceutical Industry (PSI).

I’m sure, we all face this situation sooner or later, and not surprisingly lots of research has happened in the last years in this area. In today’s episode, we will help you to understand one of the best approaches I have come across to solve this problem in a rigorous yet sophisticated way: the SIDES approach.

How many details do you need to have to call an analysis pre-specified? Should we label a request to analyze a certain subgroup by regulators as well as a fishing expedition to find a significant subgroup both in the same way: post-doc?

Recently, these problems have become much more prevalent due to the nature of composite endpoints (watch out for an interesting episode on this in a few weeks).

In this episode, we’ll give you an introduction to risk-based monitoring (RBM) as well as speak about the role of statisticians in this area. Further, we provide you as a study statistician insights into what you need to know about RBM. Finally, we also give some recommendations in terms of further resources to learn from.

This episode helps you to make your training engaging, interesting, and useful. Such training will help you to build your reputation as an expert as well as a great person to work with. It’s an awesome experience for non-statisticians if they have a light-bulb moment while being trained in statistics.

Still capturing and evaluating patient preferences does not belong to the core capabilities of most biostatisticians.

In this episode, we dig into the different aspects of this highly personal decision. Given, that Benjamin went directly into a job, whereas I first finalized my PhD, we also offer our experiences.