Validation Sampling Plans

This webinar will discuss setting up statistically justified sampling plans for process validation. Discussion will also involve using the sampling plan to set acceptance criteria for process validation. Setting acceptance criteria for test method validation will also be presented.

Why Should You Attend:

Companies in the pharmaceutical and medical device space are required to implement statistically justified sampling plans for validation. This webinar will discuss methods for setting up sampling plans depending on the risk profile of the final product or production step. It will go into using the sampling plan to set statistically justified acceptance criteria for the validation. Also presented will be setting confidence levels and spreading that confidence level out over multiple runs. Setting statistically justified acceptance criteria for test method validation will also be discussed.

All companies in the pharmaceutical and medical device space are required to implement formal and statistically justified sampling plans and acceptance criteria for validation. Many companies do not have dedicated statistics departments, so it is up to the validation or quality engineer to develop sampling plans. This training will a simple step by step method of developing statistically justified sampling plans and acceptance criteria.

Areas Covered in the Webinar:

What is Sampling

• Sampling is the ability to make a quality determination on a large number of things without direct examination of each thing

Validation Sampling

• Not the same as lot acceptance sampling
• Differences

Setting up a Validation Sampling Plan

• Pre-Sampling Determinations
• Steps to setting up sampling plans
• Variables vs Attributes Sampling Plans

The concept of Acceptance Criteria

• Variance, how much is too much
• How to measure variance and why

Use of Process Capability

• The concept of process capability
• Cp vs CpK
• How to use process capability to set acceptance criteria

Who Will Benefit:

• QA professionals
• Technical scientists
• Production staff
• Statisticians involved in validation
• from Medical Device and Pharmaceutical Companies