Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.

ABSTRACT: Detection of safety signals based on multiple comparisons of adverse events (AEs) between two treatments in a clinical trial involves evaluations requiring multiplicity adjustment. A Bayesian hierarchical mixture model is a good solution to this problem as it borrows information across AEs within the same System Organ Class (SOC) and modulates extremes due merely to chance. However, the hierarchical model compares only the incidence rates of AEs, regardless of severity. In this article, we propose a three-level Bayesian hierarchical non-proportional odds cumulative logit model. Our model allows for testing the equality of incidence rate and severity for AEs between the control arm and the treatment arm while addressing multiplicities. We conduct simulation study to investigate the operatin

SUBMITTER: Duan J 

PROVIDER: S-EPMC10552594 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature