Quantitative data mining in signal detection: the Singapore experience

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.

KEYWORDS:

• Spontaneous reports
• signal detection
• quantitative data mining (QDM)
• QDM triage
• sequential probability ratio test
• SPRT
• signals of disproportionate reporting

Author contributions

CCL conceived and designed the study, was involved in the collection, analysis, and interpretation of the data and drafted the manuscript. SS, SHT, and SR were involved in the creation of codes for QDM triage, data analysis and provided inputs in the drafting of the manuscript. PSA, SCL, and SE were involved in the data analysis and provided inputs in drafting the manuscript. All authors approved the paper as submitted and agreed to be accountable for all aspects of the work.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Supplemental material

Supplemental data for this article can be accessed here.

Additional information

Funding

This study was conducted under the SAPhIRE (Surveillance and Pharmacogenomics Initiative for Adverse Drug Reactions) Project, funded by the Biomedical Research Council of the Agency for Science, Technology, and Research of Singapore [Grant Award Number SPF2014/001]