Level: Intermediate Job Function: Analyst / Scientist / Engineer Per Vase, Managing Partner, NNE Torben Bygvraa Rasmussen, Principal Consultant, NNE Health authorities demand statistical assurance that batches after validation have sufficient quality. The Mixed Models platform in JMP can, from validation data, predict future batch performance using individual confidence intervals by setting batch as a random factor. If an individual confidence interval with alpha = acceptable quality level (AQL) falls inside the specification interval, there is less than AQL risk that a given future observation will be outside specification. In addition, individual confidence limits can be used as control limits after validation in continued process verification. Mixed models can be built for mean, moving range and range within and thereby control limits can be established for all charts in the JMP three-way chart. By using Box-Cox transformation, asymmetric limits will be obtained for the range within. Using mixed models, systematic variation in subgroups from cavities or positions, for example, can also be handled. By setting batch a systematic factor in batch release, pooling of within-batch variation is obtained, leading to reduced need for sample size. Real-world examples will be shown where a classical control limit and capability index approach will fail, but where a unified individual confidence limit approach succeeds, both concerning control limits and capability evaluation.
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Level: Intermediate Per Vase, Managing Partner, Applied Statistics Group, NNE Many companies within the pharmaceutical industry still pass a Process Performance Qualification (PPQ) simply on the fact that the estimated Ppk on three consecutive batches are better than the acceptance criteria. However, validation is about predicting the future – NOT the past. So even though the three batches may have a sufficient Ppk, this is not sufficient. The prediction for future Ppk values also needs to look promising. The International Society of Pharmaceutical Engineering (ISPE) has published a discussion paper, Evaluation of Impact of Statistical Tools on PPQ Outcomes, on how this prediction can be made from a variance component calculation of between- and within-batch variance converted to process capability indices. One of the reasons this method has not become widespread is the lack of statistical software packages with this calculation method as a standard. However, with the three-way chart in JMP 14, you can now calculate the between- and within-Sigma capability. Examples where this method has been used for PPQ evaluation will be shown in JMP 14, both with successful and unsuccessful evaluations. It will be compared with the traditional method mentioned previously.
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