I recently changed my job and moving from one company to another comes with lots of uncertainty.
Will I pick the right one?
How can I make sure, that I actually get the job, that I want?
Will hiring companies find me and offer me jobs?

For any statistician a job change represent a major shift and occurs rather infrequent – hopefully. For a recruiter – this is day-to-day business.

Look out for lots of great learnings and insights!

Have you ever considered to work with a placement student?
Do you consider to spend some time as a placement student yourself?

In this interview with Katie Thorn and Claire Brittain, we’re exploring factors, which help to make it a win for both sides. Both have worked very well together and share their stories.

I recently talked to a statistician, that initiated a small company internal group of statisticians to drive forward methodological innovation projects. While this surely will help the individuals to learn more about statistical methods, it was less clear, why and on which topics the group should focus their activities on.

While most of our day-to-day activities consists 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.

Today, we’re giving you advice on how to best pick the right projects to work on as you usually have some influence on this.

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

However, the patient might have a completely different view on the importance of the different benefits and risks. And importantly, these preferences might be less driven by biologic factors and more by personal experiences and situations as well as psychological traits. How can we assess patient preferences in this regard?

Marco Boeri and I worked on such questions in the past and some work has been presented at last years PSI conference. In todays episode, we give you some insights into what’s possible and how you can approach this problem.

Estimands continue to be a hot topic, but many statisticians struggle to put it into practice. As statistician, we wonder about the correct interpretation and how to analyse different estimands.

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).

One of the most common questions I got asked during my nearly 2 decades of being a statistician sounds similar to this: “Which patients have the best response to treatment?”
I’m sure, we all face this situation sooner or later and not surprisingly lots of research has happened in the last years on this area. In todays 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.
Both Andy Nicholls and I have applied this approach in the past and we’ll use an example, which he presented during a PSI webinar.
Listen to this episode to learn step by step how to apply the SIDES method.

Prespecified=good and post-hoc=bad. This is how we as statistician see it usually and I did too. However, over the past years I realized more and more, that it’s not that easy.
How many details do you need to have to call an analysis pre-specified? Should we label a request to analyse a certain subgroup by regulators as well as a fishing expedition to find a significant subgroup both in the same way: post-hoc?
Lovisa and I together with some others are presenting at the next PSI conference about this topic and today, we dive already into this topic and identify different dimension to be considered to understand better the different shades pre-specified analyses.
Listen to this episode to avoid oversimplification and confusion in discussions in the future.

Analysing binary or continuous data usually doesn’t cause any headaches for statisticians. But when we step into ordinal data, most of us ignore their specific nature and either dichotomize them or analyse them as if they are continuous.

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

Now Benjamin and I have worked on better tools to analyse such data already at university. We’ll dig back into what we learned then and what is still relevant today.

Risk-based monitoring plays an increasingly important role for clinical trials. Of course, the assessment of the risk is based on statistics. This presents now only interesting career options for statisticians, but also has an impact on the role of statisticians in study teams.

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.

1. Start with a relevant example
2. Collect questions upfront and track progress of answering them during the training
3. Create regular meetings to engage people
4. Interrupt your presentations with asking questions
5. Use contrasts to show the impact
6. Have a physician first introduce the example study
7. Don’t shy away from speaking to very basic things like p-values
8. Prefer white board over slides
9. Use technology for your advantage in virtual settings
10. Make pre-read easy
11. Collect feedback