5 use cases for network meta-analyses you should know about

Interview with Daniel Saure

Indirect comparisons and network-meta analyses play a rising role in our world. 

A pubmed search provides 240 hits for the term network meta-analysis in 2011. This increased to 3223 in ten years later 2021 – more than 13 times more! 

There are many problems you can solve using these approaches and statisticians overlook some on a regular basis.

Don’t miss out on providing your colleagues with great evidence (and with the ability to learn a lot about this interesting statistical approach).

Listen to my short but informative discussion with Daniel Saure as we explore five different cases with which network meta-analyses are extensively affected.

Our conversation defines the problem and solutions regarding these three primary cases:

  • Understanding your phase 3 data
  • Understanding competitive data
  • Critique published meta-analyses
  • HTA submissions
  • Reevaluation of price

Here are some lessons you can get from this conversation:

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Do you want to boost your career as a statistician in the health sector? Our podcast helps you to achieve this by teaching you relevant knowledge about all the different aspects of becoming a more effective statistician.

  1. You need to frame your data and measure the success of your product or your studies and protect the information to avoid any exploitation by other third parties that would want to use your data for their branding.
  2. Establish an effective manner by which you compare your data with the data collected by your competition.
  3. Make sure to react fast and have an existing alternative communication plan in case exploitation of data does happen.
  4. The landscape of meta-analysis continues to change depending on market demands and it is critical for statisticians to consider these adjustments to follow through the changing trend.

Head on to The Effective Statistician and learn more from what this podcast offers. Please share this with your friends and peers and learn from our conversation.

Daniel Saure
Principal Clinical Data Scientist at Boehringer Ingelheim

Below is his past achievements and positions:

  • 2012: M.Sc. Mathematics with minor International Economics, Johannes-Gutenberg-University of Mainz
  • 2012-2016: Institute of Medical Biometry and Informatics Heidelberg, Working group “Systematic reviews & meta-analyses”
  • 2016: PhD Medical Biometry on sequential meta-analyses
  • 2016-2021: Research Scientist at Eli Lilly & Company, global go-to person for Medical Affairs (MA), Health Technology Assessment (HTA), Real World Evidence (RWE) in Dermatology

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