Pharmacoepidemiological considerations in observed‐to‐expected analyses for vaccines†
نویسندگان
چکیده
Observed-to-expected (OE) analyses, together with data mining algorithms1–7 and pharmacoepidemiological studies,8 are part of the quantitative pharmacovigilance toolkit for vaccines. While data mining algorithms generate hypotheses about potential safety concerns and pharmacoepidemiological studies test specific hypotheses or measure associations, OE analyses stand in between. The role of OE analyses is to refine previously detected signals when there is not enough information to determine whether further action is necessary. In this paper, the focus is on the OE analyses of spontaneous reports, where the observed number of cases is obtained from a spontaneous reporting system and compared with the expected number of cases calculated based on background incidence rates from independent sources, such as epidemiological studies or national statistics. Note that disproportionality data mining algorithms estimate an “OE ratio” generated based on expected and observed numbers of cases from a single spontaneous reporting system. The key requirements and statistical methods recommended for OE analyses are described in European guidelines.9,10 Here, we discuss in more detail how to perform the analysis and deal with uncertainties. Although described here in the context of vaccines, the methodology and recommendations are in principle also applicable for other medicinal products, but additional complexities would then have to be considered. We will not discuss the use of OE analyses for sequential monitoring, which has been described elsewhere.11,12
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