Pharmacovigilance studies without a priori hypothesis: systematic review highlights inappropriate multiple testing correction procedures

Abstract

Objectives: The purpose of this study was to systematically review the statistical methods used in pharmacovigilance studies without a priori hypotheses.

Study design and setting: A systematic review was performed on studies published in the MEDLINE database between 2012 and 2021. The included studies were analyzed for database name and type, statistical methods, anatomical therapeutic chemical class for the studied drug(s), and SOC MedDRA classification for the studied adverse drug reaction.

Results: Ninety-two studies were included, with pharmacovigilance databases being the most used type. Disproportionality analysis using frequentist or Bayesian methods was the most common statistical method employed. The most studied drug classes were anti-infectives, nervous system drugs, and antineoplastics and immunomodulators. However, no common procedure was implemented to correct for multiple testing.

Conclusion: This review highlights the limited number of statistical methods employed for pharmacovigilance studies without a priori hypotheses, with no established consensus-based method and a lack of interest in multiple testing correction. The establishment of guidelines is recommended to improve the performance of such studies.

Keywords: Bayesian methods; Database analysis; Disproportionality analysis; Frequentist methods; Multiple testing correction; Pharmacovigilance; Signal detection; Systematic review.