Leadership

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.

Oliver has accrued lots of experience working in different setups over his career and thus can tell about the different strengths and limitations of the set-ups.

Benefit-risk assessments are made by various stakeholders including sponsors, regulators, payers, physicians, and patients throughout the life-cycle of a treatment.

We’ll explore where leadership powers originate, how they shape decision-making, and most importantly, how you can harness them to amplify your influence and effectiveness within your statistical domain.