Assessing the incidence and severity of drug adverse events: a Bayesian hierarchical cumulative logit model.
J Biopharm Stat
; 34(2): 276-295, 2024 Mar.
Article
in En
| MEDLINE
| ID: mdl-37016726
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 operating characteristics of the proposed hierarchical model. The simulation study demonstrates that the proposed method could be implemented as an extension of the Bayesian hierarchical mixture model in detecting AEs with elevated incidence rate and/or elevated severity. To illustrate, we apply our proposed method using the safety data from a phase III, two-arm randomized trial.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Logistic Models
Type of study:
Clinical_trials
/
Incidence_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
J Biopharm Stat
Journal subject:
FARMACOLOGIA
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos