Quantitative data mining in signal detection: the Singapore experience. Issue 5 (3rd May 2020) - Chan, Cheng Leng; Soh, Sally; Tan, Siew Har; Ang, Pei San; Rudrappa, Sowmya; Li, Shu Chuen; Evans, Stephen J.W.
ABSTRACT: Background : In Singapore, the Health Sciences Authority (HSA) reviews an average of 20, 000 spontaneous adverse event (AE) reports yearly. Potential safety signals are identified manually and discussed on a weekly basis. In this study, we compared the use of four quantitative data mining (QDM) methods with weekly manual review to determine if signals of disproportionate reporting (SDRs) can improve the efficiency of manual reviews and thereby enhance drug safety signal detection. Methods : We formulated a QDM triage strategy to reduce the number of SDRs for weekly review and compared the results against those derived from manual reviews alone for the same 6-month period. We then incorporated QDM triage into the manual review workflow for the subsequent two 6-month periods and made further comparisons against QDM triage alone. Results : The incorporation of QDM triage into routine manual reviews resulted in a reduction of 20% to 30% in the number of drug–AE pairs identified for further evaluation. Sequential Probability Ratio Test (SPRT) detected more signals that mirror human manual signal detection than the other three methods. Conclusions : The adoption of QDM triage into our manual reviews is a more efficient way forward in signal detection, avoiding missing important drug safety signals.