Benefits of combining change‐point analysis with disproportionality analysis in pharmacovigilance signal detection. Issue 3 (10th July 2018)

• Record Type: Journal Article
• Authors: Trinh, Nhung T.H.; Solé, Elodie; Benkebil, Mehdi
• Is Part Of: Pharmacoepidemiology and drug safety. Volume 28:Issue 3(2019)
• Page Start: 370
• Page End: 376
• Publication Date: 2018-07-10
• Subjects: change‐point analysis -- disproportionality analysis -- pharmacoepidemiology -- pharmacovigilance -- proportional reporting ratio -- signal detection; Pharmacoepidemiology -- Periodicals; Chemotherapy -- Periodicals; Epidemiology -- Periodicals; 615.705
• DOI: 10.1002/pds.4613
• Languages: English
• ISSNs: 1053-8569

Abstract

Abstract: 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.