Abstract
BackgroundData masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection. Many previous studies can be roughly classified into three categories: data removal, regression and stratification. However, frequency differences of adverse drug events (ADEs) reports, which would be an important factor of masking, were not considered in these methods. The aim of this study is to explore a novel stratification method for minimizing the impact of frequency differences on real signals masking.MethodsReports in the Chinese Spontaneous Reporting Database (CSRD) between 2010 and 2011 were selected. The overall dataset was stratified into some clusters by the frequency of drugs, ADRs, and drug-event combinations (DECs) in sequence. K-means clustering was used to conduct stratification according to data distribution characteristics. The Information Component (IC) was adopted for signal detection in each cluster respectively. By extracting ADRs from drug product labeling, a reference database was introduced for performance evaluation based on Recall, Precision and F-measure. In addition, some DECs from the Adverse Drug Reactions Information Bulletin (ADRIB) issued by CFDA were collected for further reliability evaluation.ResultsWith stratification, the study dataset was divided into 21 clusters, among which the frequency of DRUGs, ADRs or DECs followed the similar order of magnitude respectively. Recall increased by 34.95% from 29.93 to 40.39%, Precision reduced by 10.52% from 54.56 to 48.82%, while F-measure increased by 14.39% from 38.65 to 44.21%. According to ADRIB after 2011, 5 DECs related to Potassium Magnesium Aspartate, 61 DECs related to Levofloxacin Hydrochloride and 26 DECs related to Cefazolin were highlighted.ConclusionsThe proposed method is effectively and reliably for the minimization of data masking effect in signal detection. Considering the decrease of Precision, it is suggested to be a supplement rather than an alternative to non-stratification method.
Highlights
Data masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection
The conventional methods of ADR signal detection are mainly based on disproportionality analyses [2], such as Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), the integrated standard method taken by Medicines and Healthcare Products Regulatory Agency (MHRA), Information Component (IC), Multi-item Gamma Passion Shrinker (MGPS) and so on [3,4,5,6,7,8,9,10,11]
Data masking is a collateral effect of quantitative methods in signal detection, which relies on disproportionality analysis by which signals of suspected drugevent combinations (DECs) may be delayed or hindered because of the over-reporting of another DEC [20]
Summary
Data masking is an inborn defect of measures of disproportionality in adverse drug reactions (ADRs) signal detection. The conventional methods of ADR signal detection are mainly based on disproportionality analyses [2], such as Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), the integrated standard method taken by Medicines and Healthcare Products Regulatory Agency (MHRA), Information Component (IC), Multi-item Gamma Passion Shrinker (MGPS) and so on [3,4,5,6,7,8,9,10,11] These methods have achieved acceptable performance [12, 13], they are strongly affected by several biases, such as under-reporting, misdiagnosis and selective reporting [14, 15], which may lead to data masking effect [16,17,18] or competition bias [15, 19]. It should be noted that confounding could only be evaluated in the absence of effect modification [28, 31], otherwise the integrity of data would be destroyed and false signals might come
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