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Statistically Significant versus Practically Relevant Trend in Stability Data.
Bernhard Schmelzer1, AndrÉ Mischo2, Franz Innerbichler2
1Sandoz GmbH, Langkampfen, Austria bernhard.schmelzer@novartis.com.
PDA Journal of Pharmaceutical Science and Technology
|February 20, 2021
Summary
This study offers a scientific method to determine if a statistically significant trend in pharmaceutical stability data is practically relevant. It uses analytical expert assessments and over 60 data sets to establish decision criteria for trend evaluation.
Area of Science:
- Pharmaceutical Science
- Analytical Chemistry
- Biostatistics
Background:
- Assessing the practical relevance of statistically significant trends in pharmaceutical stability data is crucial for drug development and quality control.
- Existing methods may not adequately balance the risk of missing relevant trends with the risk of false positives.
Purpose of the Study:
- To develop a scientific framework for evaluating the practical relevance of statistically significant trends in pharmaceutical stability data.
- To provide decision criteria for trend assessment based on method variability and expert judgment.
Main Methods:
- Leveraged over 60 data sets from Biosimilar projects.
- Employed a panel of analytical experts to assess the practical relevance of statistically significant trends.
- Derived decision criteria for different measures of variability to align with expert assessments.
Main Results:
- Established a scientific approach to differentiate practically relevant trends from statistically significant but irrelevant ones.
- Developed recommendations for decision criteria tailored to various measures of variability.
- Demonstrated the utility of the approach through a case study.
Conclusions:
- The proposed method provides a robust way to assess the practical significance of trends in pharmaceutical stability data.
- The derived decision criteria help in making informed decisions, balancing sensitivity and specificity in trend detection.
- This approach enhances the reliability of stability data interpretation in pharmaceutical development, particularly for biosimilars.
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