Valid Statistical Rationales for Sample Sizes used in Non-Clinical Verification, Validation, and Engineering Studies


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This webinar provides guidance on how to justify sample sizes, and thereby indirectly provides guidance on how to choose sample sizes. Those justifications can then be documented in Protocols or regulatory submissions or can be given to regulatory auditors who may ask for them during onsite audits at your company. Thus, this webinar is designed to help you avoid regulatory delays in product approvals and to prevent an auditor from issuing you a nonconformity.

Why Should You Attend:

Almost all manufacturing and development companies perform at least some verification testings or validation studies of design-outputs and/or manufacturing processes, but it is sometimes difficult to explain the rationale for the sample sizes used in such efforts. This webinar provides the training in how to make and word such rationales.

NOTE: This webinar does not address rationales for sample sizes used in clinical trials.

This webinar explains the logic behind sample-size choice for several statistical methods that are commonly used in verification or validation efforts, and how to express a valid statistical justification for a chosen sample size. The statistical methods discussed during the webinar include the following:

  • Confidence intervals
  • Process Control Charts
  • Process Capability Indices
  • Confidence / Reliability Calculations
  • MTBF Studies ("Mean Time Between Failures" of electronic equipment)

QC Sampling Plans.

Areas Covered in the Webinar:

  • Introduction
    • Examples of regulatory requirements related to sample size rationale
    • Sample versus Population
    • Statistic versus Parameter
  • Rationales for sample size choices when using...
    • Confidence Intervals
      • ** attribute data
      • ** variables data
    • Statistical Process Control C harts (e.g., XbarR)
    • Process Capability Indices (e.g., Cpk )
    • Confidence/Reliability Calculations
      • ** attribute data
      • ** variables data (e.g., K-tables)
    • Significance Tests ( using t-Tests as an example )
      • ** when the "significance" is the desired outcome
      • **when "non-significance" is the desired outcome (i.e., "Power" analysis)
    • AQL sampling plans
  • Examples of statistically valid "Sample-Size Rationale" statements

Who Will Benefit:

  • QA/QC Supervisor
  • Process Engineer
  • Manufacturing Engineer
  • QC/QC Technician
  • Manufacturing Technician
  • R&D Engineer

From Design/manufacturing in the Medical Device and Pharmaceutical Industries, and other associated companies.

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