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Validation of Qualitative and Semi-Quantitative Methods: Current Practices and Future Challenges

This report discusses the validation of qualitative and semi-quantitative methods in the analytical sciences. It explores different approaches, performance criteria, and guidance documents for validation. The report also addresses special approaches for planning and data treatment when normal statistical methods are not applicable.

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Validation of Qualitative and Semi-Quantitative Methods: Current Practices and Future Challenges

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  1. WorkshopMethod Validation in Analytical SciencesCurrent practices and future challengesGent, 9-10 May 2016 Report from WG 3 Validation of qualitative and semi-quantitative methods

  2. Validation of qualitative and semi-quantitative methods • Moderator: • Steve Ellison (LGC, UK) How to validate methods where data cannot be handled by normal statistics (e.g. PCR)

  3. Suggested questions • What type of qualitative / semi-quantitative methods are you interested in validating? • What are the different approaches applied in different fields? • What are the performance criteria used to validate qualitative / semi-quantitative methods? • What are the documents available for guidance? • How do you decide about the extent of validation needed? • Examples of special approaches for planning and data treatment (when the normal approaches are not applicable – e.g. in terms of statistics for the data treatment)

  4. Actual questions • What does ‘qualitative’ mean? • What does ‘semi-quantitative’ mean? • What’s different about ‘qualitative’ validation? • What special problems does it present? • ... and what stays the same? • What does it have in common with any other method validation?

  5. What does ‘qualitative’ mean? • Qualitative • Classification (a category, not a number) • Examples: Yes/No, Present/Absent, Identity

  6. What does ‘semi-quantitative’ mean? • Ordinal scales • Absent/Low/Med/High • ‘Binned’ • 0-10, 10-100 ... • Quantitative with large uncertainty • Discussion: Hard to find purely semiquantitative examples; often quantitative expressed semiquantitatively

  7. What’s different about ‘qualitative’ validation? • What special problems does it present? • Not a number: • Results can be counted but not summarised to average, standard deviation etc. • This has considerable implications for size of experiments,

  8. ... and what stays the same? • General concepts apply: • Detection capability • Ruggedness • Selectivity/specificity • Accuracy (agreement with reference/true value) • Precision (result can be reproduced under repeatability/reproducibiiity conditions • ... but these all have quite different interpretation in qualitative context

  9. Any other comments? • Screening methods do not have the same requirements when included in a larger confirmation process • Uncertainty is difficult to express • Probabilities/response rate not very reliable • Traceability applies to test conditions • Unclear whether one can be ‘traceable’ to a reference library

  10. Any other comments? (cont) • Qualitative validation needs a lot more observations • Need to find a balance between ‘enough to detect problems’ but not so large to go out of business

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