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?)?
Some of the input data is missing for some studies. How do you deal with these missings – just exclude them?
You compare many treatments with each other in a pairwise way. How do you best communicate this large matrix of pairwise comparisons?
You need to adapt your NMA last minute because there was a delay in the system and your systematic literature search became outdated. Do you need to postpone the launch of the new product by a couple of weeks?
You got regulatory approval for a new indication earlier than expected. While the regulatory team celebrates the achievement you worry that you need to have your NMA ready 3 months earlier. Will you be able to deliver?
We discuss these and many more problems of NMAs.
Executive VP Of Launch And Commercialisation Data Sciences at Veramed
He received my PhD in biostatistics from the University of Göttingen in Germany. I have authored more than 70 scientific manuscripts in peer-reviewed journals and regularly speak at international conferences – both statistical ones like Statisticians in the Pharmaceutical Industry (PSI) and medical ones like the European Dermatology Congress (EADV).
In his 18 years of experience in the pharmaceutical industry, I focused on late-phase work. I’m leading a team of highly capable statisticians at a major pharmaceutical company. His team collaborates with a diverse set of people like physicians, marketeers, economists and many more. They leverage my leadership insights to influence widely across the business.
In addition, he serves on the board of directors of Statisticians in the Pharmaceutical Industry (PSI) as Chairman of the Communications Committee.
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