Evaluation of Impact of Statistical Tools on Process Performance Qualification (PPQ) Outcomes
In 2011, FDA issued a “Guidance for Industry Process Validation: General Principles and Practices Guidance,” which calls for a lifecycle approach to process validation and heavily references the use of statistics throughout the product lifecycle. For many, the use of statistics is new and could seem daunting due to the large number of possible statistical tools which could be used and the complexity of understanding the tools and ensuring they are appropriately applied. Statistics are a powerful tool which can enhance our level of process understanding and ultimately guide us to improve process performance and product quality/reliability.
This Discussion Paper uses segments of typical validation case studies (validation of key attributes, such as: content uniformity, packaging key attributes, and packaging critical defects) to apply various statistical tools and compares the outcomes of applying each tool pointing out the pros and cons of each application. General comment is also made on the statistical tools applied with some advantages, disadvantages, and misuses briefly summarized.
This Paper does not intend to teach or provide readers with a better understanding of the mathematics behind the tool, only to overview outcomes when applying each of the tools to the same data set. Having this comparison may help guide selection of the most appropriate tool, or in most cases combination of tools to inform the scientist validating the process of the level of process variation and control within and across batches. Readers are encouraged to work with trained statistical experts to ensure the tools are applied appropriately for their specific scenario taking into account the sample plan and intent of the analysis being conducted.
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