PMID- 37016726
OWN - NLM
STAT- MEDLINE
DCOM- 20240201
LR  - 20240218
IS  - 1520-5711 (Electronic)
IS  - 1054-3406 (Print)
IS  - 1054-3406 (Linking)
VI  - 34
IP  - 2
DP  - 2024 Mar
TI  - Assessing the incidence and severity of drug adverse events: a Bayesian 
      hierarchical cumulative logit model.
PG  - 276-295
LID - 10.1080/10543406.2023.2194385 [doi]
AB  - 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.
FAU - Duan, Jiawei
AU  - Duan J
AUID- ORCID: 0000-0001-8979-1651
AD  - Global Drug Development, Novartis Pharmaceuticals Corporation, 1 Health Plaza, 
      East Hanover, New Jersey, USA.
FAU - Gajewski, Byron J
AU  - Gajewski BJ
AD  - Department of Biostatistics & Data Science, University of Kansas Medical Center, 
      Kansas, USA.
FAU - Sen, Paramita
AU  - Sen P
AD  - Global Drug Development, Novartis Pharmaceuticals Corporation, 1 Health Plaza, 
      East Hanover, New Jersey, USA.
FAU - Wick, Jo A
AU  - Wick JA
AD  - Department of Biostatistics & Data Science, University of Kansas Medical Center, 
      Kansas, USA.
LA  - eng
GR  - P30 CA168524/CA/NCI NIH HHS/United States
PT  - Journal Article
DEP - 20230404
PL  - England
TA  - J Biopharm Stat
JT  - Journal of biopharmaceutical statistics
JID - 9200436
SB  - IM
MH  - Humans
MH  - Bayes Theorem
MH  - Computer Simulation
MH  - Incidence
MH  - *Logistic Models
MH  - Probability
MH  - Clinical Trials, Phase III as Topic
MH  - Randomized Controlled Trials as Topic
PMC - PMC10552594
MID - NIHMS1885715
OTO - NOTNLM
OT  - Bayesian mixture model
OT  - Drug safety
OT  - multiplicity
OT  - non-proportional odds cumulative logit model
OT  - safety signal detection
EDAT- 2023/04/06 06:00
MHDA- 2024/01/23 06:43
PMCR- 2025/03/01
CRDT- 2023/04/05 01:52
PHST- 2025/03/01 00:00 [pmc-release]
PHST- 2024/01/23 06:43 [medline]
PHST- 2023/04/06 06:00 [pubmed]
PHST- 2023/04/05 01:52 [entrez]
AID - 10.1080/10543406.2023.2194385 [doi]
PST - ppublish
SO  - J Biopharm Stat. 2024 Mar;34(2):276-295. doi: 10.1080/10543406.2023.2194385. Epub 
      2023 Apr 4.