Statistical Justification for Sample Size and the Use of Only 3 Lots for Process Validation

MessageThis Webinar is over
Date Sep 27, 2018
Time (1:00 pm to 2:00 pm)
Cost $200.00
Online
This webinar provides a “statistical” justification and method for determining Sample Sizes, and a statistical justification for using only 3 Lots (which is the typical number, especially in industries regulated by the FDA).

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.

This webinar does not address clinical trials, nor bulk-solution processes. It applies to unitized products such as pills, drug-filled syringes, medical devices, and components.

Areas Covered in the Session :
This webinar explains how to choose and justify a sample-size for Lots that are included in Process Validation studies. The statistical methods discussed during the webinar include the following:
  • Confidence intervals
  • Confidence / Reliability Calculations (for variables & attributes)
It then explains how to analyze those samples in such a way that they provide statistically valid final %Reliability for the production Process itself. One example is worked through completely.
Topics include:
  • Introduction:
    • Regulatory requirements
    • Basic concepts and vocabulary
  • Calculation of Sample Size to be taken from each Lot in the Validation study
  • Calculation of % Confidence and %Reliability ( = %-in-specification) for each Lot
  • Calculation of % confidence and %Reliability for the Production Process
  • Worked example (with all calculations)
  • Example summary “justification” statement
  • Access to instructor’s website, for downloading free relevant statistical software
Who Should Attend:
  • Research and Development Departments
  • Quality Assurance Departments
  • Quality Control Departments
  • Manufacturing Departments
  • Engineering Departments

 


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