2015
DOI: 10.1248/bpb.b15-00253
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Analysis of Neuropsychiatric Adverse Events in Patients Treated with Oseltamivir in Spontaneous Adverse Event Reports

Abstract: There have been concerns that oseltamivir causes neuropsychiatric adverse events (NPAEs). We analyzed the association of age and gender with NPAEs in patients treated with oseltamivir using a logistic regression model. NPAE data were obtained from the U.S. Food and Drug Administration Adverse Event Reporting System (2004 to 2013). The lower limit of the reporting odds ratio (ROR) 95% confidence interval (CI) of "abnormal behavior" in Japan, Singapore, and Taiwan was ≥1. The effects of the interaction terms for… Show more

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Cited by 25 publications
(13 citation statements)
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“…ROR was developed to detect drug-associated adverse events in the spontaneous reporting database 32 34 . To calculate the ROR, all reported adverse events associated with nephritis were defined as “cases” and all other reported adverse events as “non-cases”.…”
Section: Methodsmentioning
confidence: 99%
“…ROR was developed to detect drug-associated adverse events in the spontaneous reporting database 32 34 . To calculate the ROR, all reported adverse events associated with nephritis were defined as “cases” and all other reported adverse events as “non-cases”.…”
Section: Methodsmentioning
confidence: 99%
“…Pharmacovigilance indexes, such as the ROR, have been developed to detect drug‐associated adverse events in the SRS, but these cannot be applied to evaluate the comparative strength of causalities (Bates & Evans, ). The ROR is a clear and easily applicable technique that allows for the control of covariates through logistic regression analysis (Abe et al., ; Suzuki et al., ; Ueda et al., ; Umetsu et al., ). An additional advantage of using the ROR is that non‐selective underreporting of a drug or adverse events has no influence on the value of the ROR compared with the population of patients experiencing an adverse event (Van der Heijden, van Puijenbroek, van Buuren, & van der Hofstede, ).…”
Section: Discussionmentioning
confidence: 99%
“…The FAERS database is subject to various biases such as under-reporting, over-reporting, exclusion of healthy individuals, and confounding by comorbidities [ 18 ]. The data subsetting strategy may help to mitigate the effect of confounding factors and bias on signal detection by limiting the analysis to a population of patients that are thought to share common risk factors and diseases [ 15 , 16 , 34 36 ].…”
Section: Discussionmentioning
confidence: 99%