Choosing the Best Device Sample Size for Verification and Validation - Webinar CD/Transcript

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Choosing the Best Device Sample Size for Verification and Validation: Tools to Safely Speed Your Device to Market

When performing process validations the question frequently asked is “How many do I need?”

The answer is — it depends. There are several factors that must be considered when determining the appropriate sample size, including risks assessment, production costs, inspection costs, measuring, and testing.

If you’re like many manufacturers, you understand the essence of the 21 CFR 820.30 requirement — you must run enough test samples of a product so its test results can be successfully applied to full-scale production runs. (Also, your sample size must be appropriate for the type of testing you’re doing and the type of product.)

And, like many manufacturers, you’ve probably had trouble determining exactly how many units of a product you should test to satisfy the FDA.

Join design control statistical expert Steve Walfish as he helps you understand exactly what sample size depends on. During his presentation, you will learn:

  • The requirements for statistical techniques and how they impact design control processes (21 CFR 820.30(f)(g))
  • What types of requirements lend themselves to statistics in verification and validation (hypothesis testing, confidence interval, design of experiments)
  • How variance in the population can impact the sample size necessary to establish objective evidence
  • The relationship between risk and sample size (i.e., risk to patient — critical major, minor)

You will also gain the fundamental knowledge you need to determine sample size in statistical testing. (For example, a sample size of 3 is not sufficient without justification.)

In addition, Mr. Walfish will cover the following:

  • Why it is critical to understand the different compliance requirements for design verification and design validation — and how to understand those differences
  • Leveraging statistical methods that work best to satisfy the FDA’s requirements for defensible methods
  • How to use proven methodologies to avoid too small — or too large — sample sizes. 
    (Too small and you might not be accurately determining risk; too large and you could be unnecessarily wasting time and money.)
  • How sample size should optimally be proportional to risk (business and patient)
  • Why it’s pointless to try and predict the personal focus of different auditors — and why the real foundation for a successful audit is being able to produce a defensible program based on visible standards

You'll learn proven tactics and tools to develop a strong statistical methods program — and a thorough understanding of what FDA auditors look for when they come for an inspection at your facility.

  • Validation and verification professionals
  • Quality engineering
  • Regulatory Affairs
  • QA/QC
  • Software development, programming, documentation, testing
  • R&D
  • Engineering
  • Production
  • Operations

Steven Walfish is the president of Statistical Outsourcing Services, a consulting company that provides statistical analysis and training to a variety of industries. Prior to starting Statistical Outsourcing Services, he was the Senior Manager Biostatistics, Non-clinical, at Human Genome Sciences in Rockville, MD. Prior to joining HGS, he was a senior associate at PricewaterhouseCoopers specializing in the pharmaceutical industry.