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Content provided by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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Learnings from the COVID crisis for statisticians

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Manage episode 257356147 series 2400265
Content provided by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Click this link to go to the homepage of this episode!

I discuss my learnings, my reflection about this, and what is the meaning of this situation for us, statisticians, and more significant factors like the following:

  • Hardly any tables out there, but lots of visualizations both interactive and explanatory - still most of our day-to-day jobs are providing tables
  • Get the data from many different sources - not just the one source at hand: e.g. the study currently working on; RWE, literature data, and other studies
  • Many misunderstandings on the sources of the data and comparisons across different sources (e.g. different countries) and then infer on the differences between the policy making in the different countries - where are the statisticians in the news explaining the numbers?
    • We need more leadership
    • Our associations need to step up and become more professional and impactful
    • We have a responsibility here
  • Responsibility for providing the numbers but also gives the background and advise on how to use the numbers - for example John Hopkins
    • FAQ not really helpful
    • What can be answered here and what not
    • How reliable is the data?
    • What are the strength and limitations?
    • Any guidance on the use of the data?
  • Do we train people on how to read our data? Or do we just through the tables over the fence for someone else to deal with it?
  • Power of scenario simulations - Washington Post article - great to show conditional probabilities e.g. if you have different prior information in Bayesian analyses

Listen to this timely episode and let me know what you think!

  continue reading

402 episodes

Artwork
iconShare
 
Manage episode 257356147 series 2400265
Content provided by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Alexander Schacht and Benjamin Piske, biometricians, statisticians and leaders in the pharma industry, Alexander Schacht, Benjamin Piske, and Leaders in the pharma industry or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.

Click this link to go to the homepage of this episode!

I discuss my learnings, my reflection about this, and what is the meaning of this situation for us, statisticians, and more significant factors like the following:

  • Hardly any tables out there, but lots of visualizations both interactive and explanatory - still most of our day-to-day jobs are providing tables
  • Get the data from many different sources - not just the one source at hand: e.g. the study currently working on; RWE, literature data, and other studies
  • Many misunderstandings on the sources of the data and comparisons across different sources (e.g. different countries) and then infer on the differences between the policy making in the different countries - where are the statisticians in the news explaining the numbers?
    • We need more leadership
    • Our associations need to step up and become more professional and impactful
    • We have a responsibility here
  • Responsibility for providing the numbers but also gives the background and advise on how to use the numbers - for example John Hopkins
    • FAQ not really helpful
    • What can be answered here and what not
    • How reliable is the data?
    • What are the strength and limitations?
    • Any guidance on the use of the data?
  • Do we train people on how to read our data? Or do we just through the tables over the fence for someone else to deal with it?
  • Power of scenario simulations - Washington Post article - great to show conditional probabilities e.g. if you have different prior information in Bayesian analyses

Listen to this timely episode and let me know what you think!

  continue reading

402 episodes

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