Tree-based scan statistic - Application in manufacturing-related safety signal detection.

Background And Objectives: Over the last decades, medicinal regulations have been put into place and have considerably improved manufacturing practices. Nevertheless, safety issues may still arise. Using the simulation described in this manuscript, our aim is to develop adequate detection methods for manufacturing-related safety signals, especially in the context of biological products.

Methods: Pharmaceutical companies record the entire batch genealogies, from seed batches over intermediates to final product (FP) batches. We constructed a hierarchical tree based on this genealogy information and linked it to the spontaneous safety data available for the FP batch numbers. The tree-based scan statistic (TBSS) was used on simulated data as a proof of concept to locate the source that may have subsequently generated an excess of specific adverse events (AEs) within the manufacturing steps, and to evaluate the method's adjustment for multiple testing. All calculations were performed with a customized program in SAS v9.2.

Results: The TBSS generated a close to expected number of false positive signals, demonstrating that it adjusted for multiple testing. Overall, the method detected 71% of the simulated signals at the correct production step when a 6-fold increase in reports with AEs of interest (AEOI) was applied, and 31% when a 2-fold increase was applied. The relatively low detection performance may be attributed to the higher granularity associated with the lower levels of the hierarchy, leading to a lack of power and the stringent definition criteria that were applied for a true positive result.

Conclusion: As a data-mining method for manufacturing-related safety signal detection, the TBSS may provide advantages over other disproportionality analyses (using batch information) but may benefit from complementary methods (not relaying on batch information). While the method warrants further refinement, it may improve safety signal detection and contribute to improvements in the quality of manufacturing processes.