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A Guide to Sample Size for Non-Inferiority Studies

A Guide to Sample Size for Non-Inferiority Studies
2:30
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Sample Size for Non-inferiority Studies 
Design Considerations and Dealing with Non-Continuous Endpoints

In this tutorial, we have explored how to determine the appropriate sample size for non-inferiority studies. Non-inferiority means testing if a proposed treatment is no worse than an existing approach by showing it is above the non-inferiority bound. 

You will learn about: 

  • Introduction to Non-inferiority Designs

  • Important Design Considerations for Non-inferiority Designs

  • Sample Size Determination for Non-inferiority Designs

Sample Size for Non-inferiority Studies

Non-inferiority testing is used to test if a new treatment is not inferior to a standard treatment. This is a common objective in areas such as medical devices and generic drug development. For example, if a proposed device or treatment were less invasive than the standard treatment then non-inferiority would be an appropriate route to improve patients’ treatment choices. 

To test for non-inferiority, the treatment group is tested to verify it is above the non-inferiority margin. The non-inferiority margin is a level below equality that would still be considered acceptably non-inferior to the standard treatment. The definition of the non-inferiority margin is a matter of significant debate and is an important aspect of regulatory guidance from agencies such as the FDA.

Non-inferiority studies can be conducted for a wide variety of different endpoints including continuous, binomial, survival and count endpoints. Each of these endpoints present unique design and statistical considerations with a wide range of potential design choices. For example, common designs for non-inferiority are crossover designs, three arm trials and parallel arm trials.

In this tutorial, we have reviewed non-inferiority testing design considerations and demonstrated how to determine the sample size for a wide variety of endpoints and designs.

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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