| アブストラクト | As a national public health surveillance resource, Vaccine Adverse Event Reporting System (VAERS) is a key component in ensuring the safety of vaccines. Numerous methods have been used to conduct safety studies with the VAERS database. These efforts focus on the downstream statistical analysis of the vaccine and adverse event associations. In this paper, we primarily focus on processing the raw data in VAERS before the analysis step, which is also an important part of the signal detection process. Due to the semi-annual update in the Medical Dictionary for Regulatory Activities (MedDRA) coding system, adverse event terms that describe the same symptom might change in VAERS; therefore, we identify these terms and combine them to increase the signal detection power. We also consider the uncertainty of the vaccine and adverse event pairs that arise from reports with multiple vaccines. Finally, we discuss four commonly used statistics in assessing the vaccine and adverse event associations, and propose to use the statistics that are robust to the reporting bias in VAERS and adjust for potential confounders of the vaccine and adverse event association to increase signal detection accuracy. |
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| 組織名 | Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.;Department of Statistics, University of Michigan, Ann Arbor, Michigan, USA.;Division of Tuberculosis Elimination, Centers for Disease Control and Prevention,;Atlanta, GA, USA.;Department for Computational Medicine and Bioinformatics, University of Michigan,;Ann Arbor, Michigan, USA.;Department of Microbiology and Immunology, and Center for Computational Medicine;and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.;Department of Pediatrics and Department of Health Management and Policy,;University of Michigan, Ann Arbor, Michigan, USA. |
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