Background And Aim: Disproportionality analysis is traditionally used in spontaneous reporting systems to generate working hypotheses about potential adverse drug reactions: the so-called disproportionality signals. We aim to map the methods used by researchers to assess and increase the validity of their published disproportionality signals.

Methods: From a systematic literature search of published disproportionality analyses up until 1 January 2020, we randomly selected and analyzed 100 studies. We considered five domains: (1) rationale for the study, (2) design of disproportionality analyses, (3) case-by-case assessment, (4) use of complementary data sources, and (5) contextualization of the results within existing evidence.

Results: Among the articles, multiple strategies were adopted to assess and enhance the results validity. The rationale, in 95 articles, was explicitly referred to the accrued evidence, mostly observational data (n = 46) and regulatory documents (n = 45). A statistical adjustment was performed in 34 studies, and specific strategies to correct for biases were implemented in 33 studies. A case-by-case assessment was complementarily performed in 35 studies, most often by investigating temporal plausibility (n = 26). Complementary data sources were used in 25 articles. In 78 articles, results were contextualized using accrued evidence from the literature and regulatory documents, the most important sources being observational (n = 45), other disproportionalities (n = 37), and case reports (n = 36).

Conclusions: This meta-research study highlighted the heterogeneity in methods and strategies used by researchers to assess the validity of disproportionality signals. Mapping these strategies is a first step towards testing their utility in different scenarios and developing guidelines for designing future disproportionality analysis.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442263PMC
http://dx.doi.org/10.1007/s40264-023-01329-wDOI Listing

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