2023
DOI: 10.3389/fphar.2023.1259908
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A pharmacovigilance study of etoposide in the FDA adverse event reporting system (FAERS) database, what does the real world say?

Zhiwei Cui,
Feiyan Cheng,
Lihui Wang
et al.

Abstract: Introduction: Etoposide is a broad-spectrum antitumor drug that has been extensively studied in clinical trials. However, limited information is available regarding its real-world adverse reactions. Therefore, this study aimed to assess and evaluate etoposide-related adverse events in a real-world setting by using data mining method on the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database.Methods: Through the analysis of 16,134,686 reports in the FAERS database, a total of 9,892… Show more

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Cited by 13 publications
(2 citation statements)
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“…The onset time is characterized as the time span from START_DT (start date for Tivozanib use) and EVENT_DT (date of AE occurrence). Weibull distribution can ascertain and forecast the fluctuating rise or fall in risk incidence over time, utilizing scale α) and shape β) as key factors to characterize the Weibull distribution’s form ( Mazhar et al, 2021 ; Cui et al, 2023 ). The primary severe consequences were life-threatening incidents or those leading to hospitalization, disability, or death.…”
Section: Methodsmentioning
confidence: 99%
“…The onset time is characterized as the time span from START_DT (start date for Tivozanib use) and EVENT_DT (date of AE occurrence). Weibull distribution can ascertain and forecast the fluctuating rise or fall in risk incidence over time, utilizing scale α) and shape β) as key factors to characterize the Weibull distribution’s form ( Mazhar et al, 2021 ; Cui et al, 2023 ). The primary severe consequences were life-threatening incidents or those leading to hospitalization, disability, or death.…”
Section: Methodsmentioning
confidence: 99%
“…The onset time is characterized as the time span from START_DT (start date for Tivozanib use) and EVENT_ DT (date of AE occurrence). Weibull distribution can ascertain and forecast the fluctuating rise or fall in risk incidence over time, utilizing scale α) and shape β) as key factors to characterize the Weibull distribution's form (Mazhar et al, 2021;Cui et al, 2023). The primary severe consequences were life-threatening incidents or those leading to hospitalization, disability, or death.…”
Section: Algorithms Equation Criteriamentioning
confidence: 99%