title: A Bayesian group sequential approach to safety signal detection.
abstract: Clinical safety data, usually reported as clinically manifested adverse events (AEs) according to the Medical Dictionary for Regulatory Activities (MedDRA), are routinely collected during the course of a clinical trial involving comparative groups, and periodical monitoring of the safety events is often required to determine whether excessive occurrence of a set of AEs is associated with treatment. To accommodate the structure of reported AEs with the MedDRA system, a Bayesian hierarchical model has been proposed for the analysis of clinical safety data. However, the characteristics of sequential use of the Bayesian method has not been studied. In this paper the Bayesian hierarchical model is applied in a group sequential manner for multiple interim analyses of safety events. A decision-theoretic approach is employed to determine threshold values in the safety signaling process. The proposed approach is illustrated through simulations and a real example.
authors: Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
authors: Wenfeng
authors: W
authors: Chen
authors: Naiqing
authors: N
authors: Zhao
authors: Guoyou
authors: G
authors: Qin
authors: Jie
authors: J
authors: Chen
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards; D001499:Bayes Theorem; D006801:Humans; D022542:Measles-Mumps-Rubella Vaccine / Q000009:adverse effects; D016032:Randomized Controlled Trials as Topic / Q000009:adverse effects / Q000379:methods; D012107:Research Design / Q000592:standards; D017603:Signal Detection, Psychological
chemical_list: D022542:Measles-Mumps-Rubella Vaccine