These are all important questions for statistical and data science practitioners, and exploring them further could reveal opportunities for novel ways to approach problems in an increasingly complex field. So, share this link with your colleagues!

We also discuss the following points:

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We also talk about alternative approaches and some rare cases, where single arm studies might be helpful.

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References:

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Making sure that your research is reproducible is important in the pharma industry because it allows others to understand what you have done and how you arrived at those conclusions. By following these three steps—documenting everything, creating reusable code, and sharing results—you will be able to make sure that your research is reproducible for anyone who needs access to it now or in the future!

Resource:

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The EU HTA will not only affect all statisticians in the pharmaceutical industry with respect to skill sets and collaboration (we talked about that in podcast #3), but will also have an impact on the economic modeling that is needed for the reimbursement and pricing decisions in many European countries. Understanding the influence of the joint clinical assessment on economic modeling, the relationship between estimands and PICOs as well as the pre-specification of statistical analyses and their use in economic modeling is becoming much more important for statisticians in the near future.

In this episode, we will talk about the role of economic modeling in the HTA process, and the influence of the EU HTA, and the corresponding statistical analyses on that process.