Background: Change-point analysis (CPA) is a powerful method to analyse pharmacovigilance data but it has never been used on the disproportionality metric.
Objectives: To optimize signal detection investigating the interest of time-series analysis in pharmacovigilance and the benefits of combining CPA with the proportional reporting ratio (PRR).
Methods: We investigated the couple benfluorex and aortic valve incompetence (AVI) using the French National Pharmacovigilance and EudraVigilance databases: CPA was applied on monthly counts of reports and the lower bound of monthly computed PRR (PRR-). We stated a CPA hypothesis that the substance-event combination is more likely to be a signal when the 2 following criteria are fulfilled: PRR- is greater than 1 with at least 5 cases, and CPA method detects at least 2 successive change points of PRR- which made consecutively increasing segments. We tested this hypothesis by 95 test cases identified from a drug safety reference set and 2 validated signals from EudraVigilance database: CPA was applied on PRR-.
Results: For benfluorex and AVI, change points detected by CPA on PRR- were more meaningful compared with monthly counts of reports: More change points detected and detected earlier. In the reference set, 14 positive controls satisfied CPA hypothesis, 6 positive controls only met first requirements, 3 negative controls only met first requirement, and 2 validated signals satisfied CPA hypothesis.
Conclusions: The combination of CPA and PRR represents a significant advantage in detecting earlier signals and reducing false-positive signals. This approach should be confirmed in further studies.
Keywords: change-point analysis; disproportionality analysis; pharmacoepidemiology; pharmacovigilance; proportional reporting ratio; signal detection.
© 2018 John Wiley & Sons, Ltd.