Do you know what your external profile is?
Do you have an idea of how to improve it?

In today's episode, Liz Cole and I will dive deep into a topic called content marketing. It’s kind of meta, since this podcast itself originates on these ideas. Does this sound frightening or disturbing? It shouldn’t.

With so many recent advances in AI, it is important for statisticians to both keep up to date with the most recent methods and getting involved in guiding their application to the most pressing statistical challenges.

In today's episode, Karim and I cover cutting edge examples of how data science and statistical sciences are intersecting. Learn from this episode why different approaches matter when looking at clinical development data.

Do you feel you're working like a firefighter?
Do you have the perception, you're solving the same problems over and over?
Do you worry about ever-changing requests from your business partners?

Then you are not alone - not even alone in the pharma world.

In today's episode, we will talk about the data ops manifesto which addresses such problems. There are actually a couple of such manifestos out there such as the agile manifesto or the dev ops manifesto. These sound similar to Demings 14 principles outlined in his “out of the crisis” publication.

6 effective leadership behaviours for statisticians

One of my favourite leadership podcast run by Olaf Kapinski - unfortunately in German - had a great episode recently (see here for the link to the Leben-Führen podcast: https://leben-fuehren.de/5-wirksame-verhalten-fuer-fuehrungskraefte/?pk_campaign=podcast&pk_kwd=5-wirksame-verhalten-fuer-fuehrungskraefte)

Today, we’re reflecting on the 5 behaviours Olaf mentioned and add a great 6th one, which is very relevant as well.

Can you really do a bigger role?
Did you have a moment in your career, where you were thinking that you as a statistician is put in a box or labelled in such a way that limits your influence?

Only specialized statisticians discussed indirect comparisons in the past but over the years the topic developed into something, every statistician should know about.

In this episode, Benjamin and I talk about the important reasons for using indirect comparison (IC).

During this episode, we will review some of the highlights from this year. It’ll help you to remember a couple of lessons learned or inspire you to listen to some episodes (again).

So, hopefully you’ve listened to all of our other podcasts and you are still interested in applying for a job as a medical statistician…

This episode will give you all the information you need to find out how to apply, CV tips and what to expect at the interview, and what to do and what not to do!

I once reviewed tables for a randomized study and noticed several comments about testing the baseline characteristics. The commenters were arguing which test would be best to test for the differences between the 2 randomized groups at baseline.

This made my first angry about the wasted time and then curious about the reasons, statisticians still do this.

In today's episode, Benjamin and I discuss some backgrounds for baseline testing in randomized studies.

Listen to this episode, share it with others who might learn from it, and be an effective statistician!

What's interesting about the early phase?
What are the typical decisions for phase II?
Speaking about a new indication, how do you see decisions are being made across the industry?
How should we think about phase II studies in this setting?
What statistical approaches do we have to formulate this thinking?