On-Demand Webinar

A Guide to Powering Phase IV Clinical Trials

A Guide to Powering Phase IV Clinical Trials
2:08
Download and explore the data featured in this webinar:
  • One Arm Cohort Phase IV Study - Example 1.nqt
  • One Arm Cohort Phase IV Study - Example 2.nqt
  • Two Arm Cohort Phase IV Study.nqt
  • Case-Control Phase IV Study.nqt

Powering Phase IV Clinical Trials
Design Considerations for Post-Marketing Surveillance Trials

Clinical trials are primarily involved in establishing the safety and efficacy of candidate therapeutics to a high degree to allow for regulatory approval and usage in real-world patients. However, regulators still have a significant interest in ensuring the safety of therapeutics post-approval.

Therefore, post-marketing surveillance studies are a vital tool to verify the efficacy, safety and potential additional benefits of therapeutics in clinical practice - with a particular interest in finding and investigating any rare side effects.

You will learn about:

Powering Phase IV Clinical Trials

Clinical trials are primarily involved in establishing the safety and efficacy of candidate therapeutics to a high degree to allow for regulatory approval and usage in real-world patients. 

However, regulators still have a significant interest in ensuring the safety of therapeutics post-approval. Therefore, post-marketing surveillance studies are a vital tool to verify the efficacy, safety and potential additional benefits of therapeutics in clinical practice - with a particular interest in finding and investigating any rare side effects.

However, the design of post-marketing surveillance studies is highly variable with many having inadequate design or are underpowered for their objective(s) of interest. Better consideration of design and appropriate sample size determination could lead to an improved Phase IV trial performance. 

Watch this tutorial, as we cover the purpose of Phase IV trials, common design considerations for post-marketing surveillance trials and sample size determination for testing for rare side effect-related outcomes.


nQuery helps make your clinical trials faster, less costly and more successful with tools for sample size calculation - refining Frequentist, Bayesian & Adaptive designs

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Who is this for?

This will be highly beneficial if you're a biostatistician, scientist, or clinical trial professional that is involved in sample size calculation and the optimization of clinical trials in:

 

  • Pharma and Biotech
  • CROs
  • Med Device
  • Research Institutes
  • Regulatory Bodies
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