What can go wrong with network meta-analyses?

You often have multiple endpoints but these are not collected consistently across all the studies included in the NMA. How do you deal with this?

Even with the same variables assessed over studies, they might be collected at different time points. This often has an effect on both efficacy and safety results. What do you do?

Your placebo treatment might change over time and consequently your placebo response. Often placebo plays a crucial part in your network, but can you really pool all your placebo arms? The patient population differs between studies and hence between treatments. How do you control this bias?

The pool of relevant treatments differs across countries when using network meta-analyses for HTA submissions. Should you adapt your NMA for each country's submission?

You’re not submitting all your HTA dossiers at once and thus the literature search needs to be updated repeatedly hence you input data into the NMA. How big are the differences and how long will it take to update everything (and what will it cost?)?

In today's episode, I am the interviewee, and Jenny is the one asking the questions. We are talking about how training in launch and commercialization can be a super weapon, what its role is, and how can this be developed and administered for non-statisticians.

We also talk about the following interesting points:

The world has changed, and it will not turn back to what is used to be before the pandemic. This comes on top of other trends, like the increased use of data across all aspects of data. The hunger for talent in the data science space has dramatically increased, and it is not only coming from the Apples, Googles, and Amazons.

Other trends are the increase in prices, the shifts in motivations of employees, and the debate about what really makes people join and stay at companies.

I first became aware of these sources for real-world evidence some years ago, and I tried to get an expert on this topic so often, but unfortunately no one would speak with me. Then finally someone agreed when they found out what country my research was being conducted in-the Scandinavian Union! You might be wondering why we need data from such a unique place as Scandinavia? Stay tuned because you'll find out soon enough… This region provides us statisticians everything we could hope: perfect measurements plus high-quality counts.

Is a board of directors essential for everyone?
Is it necessary for it to come from within your company?

This episode is all about career advise. Together with Heather, we talk about having your personal board of directors and how could this concept help you have a career advancement overtime.

We all need to present frequently and the more you progress with your career the more important the presentations become. Messing these presentations up - especially in front of those responsible for your next promotion - will lead to bad career outcomes.

What are biomarkers, and why is this essential?

Today’s episode is an extract from my interview with Jenn Fenwick, who's also a podcaster like me and we talk about my career and leadership.

This is a fun chat where we talk about navigating fear, opening ourselves up to vulnerability and making courageous decisions… oh and Beyonce gets a mention too!

What is extrapolation?
How can we use extrapolation in paedriatics?
What are the main challenges?

Paedriatic research always comes with challenges and understanding paedriatic submission is very important. There's always a lack of treatment in this area. In this episode, you'll understand what you can do to get evidence through extrapolation for the children population.

What are the benefits of including external data in the design and analysis of clinical studies?
How does the meta-analytic predictive model (MAP) work?

I know you've had this dilemma of either having had the difficulty in collecting data or having too much available and yet irrelevant data while trying to conduct a clinical trial. When is it appropriate to use historical data and when is it appropriate to enroll new patients to collect new data?

Today, Gaëlle and I talked about the main objective of using historical data in clinical trials.