12/5/2023 0 Comments Masking dredging data researchA significant number of baseline SDRs were also lost in the course of our experiments. The SU-based unmasking protocols generated variable numbers of masked SDRs ranging from 38 to 156, representing a 0.43-1.8% increase over the number of baseline SDRs. The original disproportionality analysis identified 8719 SDRs for the 63 PTs. We assessed the strength of external evidence for a causal association for a small number of masked SDRs involving a subset of 29 drugs for which level of evidence adjudication was available from a previous study. This entailed testing every drug and drug group to determine which was most influential in uncovering masked SDRs. In order to assess the possible extent of residual masking we performed two supplemental purely empirical analyses on a limited subset of data. We performed a series of unmasking experiments selecting drugs for removal using rational statistical decision rules based on the requirement of a reporting ratio (RR) >1, top-ranked statistical unexpectedness (SU) and relatedness as reflected in the WHO Anatomical Therapeutic Chemical level 4 (ATC4) grouping. We specifically sought masking in which drug removal induced an increase in the statistical representation of a drug-event combination (DEC) that resulted in the emergence of a new SDR. For a set of 63 Medical Dictionary for Regulatory Activities (MedDRA®) Preferred Terms (PTs), disproportionality analysis was carried out with respect to all drugs included in the AERS database, using a previously described urn-model-based algorithm. This study employed AERS data from 2001 through 2005. To explore the nature, extent and possible consequences of masking in the US FDA Adverse Event Reporting System (AERS) database by applying various experimental unmasking protocols to a set of drugs and events representing realistic pharmacovigilance analysis conditions. Masking has been predicted theoretically, observed anecdotally or studied to a limited extent in both pharmaceutical company and health authority databases, but no previous publication systematically assesses its occurrence in a large health authority database. A phenomenon of 'masking' or 'cloaking' in pharmacovigilance data mining has been described, which can potentially cause signals of disproportionate reporting (SDRs) to be missed, particularly in pharmaceutical company databases.
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