Benefits of combining change-point analysis with disproportionality analysis in pharmacovigilance signal detection.

Abstract

Change-point analysis (CPA) is a powerful method to analyse pharmacovigilance data but it has never been used on the disproportionality metric. 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). 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-. 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. 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.