BAHAMA: A Bayesian Hierarchical Model for the Detection of MedDRA®-Coded Adverse Events in Randomized Controlled Trials

Autor: Alma Revers, Michel H. Hof, Aeilko H. Zwinderman
Přispěvatelé: Epidemiology and Data Science, Graduate School, APH - Methodology
Rok vydání: 2022
Předmět:
Zdroj: Drug safety, 45(9), 961-970. Adis International Ltd
ISSN: 1179-1942
0114-5916
DOI: 10.1007/s40264-022-01208-w
Popis: Introduction: Patients participating in randomized controlled trials (RCTs) are susceptible to a wide range of different adverse events (AE) during the RCT. MedDRA® is a hierarchical standardization terminology to structure the AEs reported in an RCT. The lowest level in the MedDRA hierarchy is a single medical event, and every higher level is the aggregation of the lower levels. Method: We propose a multi-stage Bayesian hierarchical Poisson model for estimating MedDRA-coded AE rate ratios (RRs). To deal with rare AEs, we introduce data aggregation at a higher level within the MedDRA structure and based on thresholds on incidence and MedDRA structure. Results: With simulations, we showed the effects of this data aggregation process and the method's performance. Furthermore, an application to a real example is provided and compared with other methods. Conclusion: We showed the benefit of using the full MedDRA structure and using aggregated data. The proposed model, as well as the pre-processing, is implemented in an R-package: BAHAMA.
Databáze: OpenAIRE
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