PMID- 33091588
OWN - NLM
STAT- MEDLINE
DCOM- 20210924
LR  - 20210924
IS  - 1559-2030 (Electronic)
IS  - 1551-7144 (Linking)
VI  - 99
DP  - 2020 Dec
TI  - Bayesian hierarchical model for safety signal detection in multiple clinical 
      trials.
PG  - 106183
LID - S1551-7144(20)30261-5 [pii]
LID - 10.1016/j.cct.2020.106183 [doi]
AB  - Clinical safety signal detection is of great importance in establishing the 
      safety profile of new drugs and biologics during drug development. Bayesian 
      hierarchical meta-analysis has proven to be a very effective method of 
      identifying potential safety signals by considering the hierarchical structure of 
      clinical safety data from multiple randomized clinical trials conducted under an 
      Investigational New Drug (IND) application or Biological License Application 
      (BLA). This type of model can integrate information across studies, for instance 
      by grouping related adverse events using the MedDRA system-organ-class (SOC) and 
      preferred terms (PT). It therefore improves the precision of parameter estimates 
      compared to models that do not consider the hierarchical structure of the safety 
      data. We propose to extend an existing four-stage Bayesian hierarchical model and 
      consider the exposure adjusted incidence rate, assuming the number of adverse 
      events (AEs) follows a Poisson distribution. The proposed model is applied to a 
      real-world example, using data from three randomized clinical trials of a 
      neuroscience drug and examine in three simulation studies motivated by real-world 
      examples. Comparison is made between the proposed method and other existing 
      methods. The simulation results indicate that our proposed model outperforms 
      other two candidate models in terms of power and false detection rate.
CI  - Copyright (c) 2020 Elsevier Inc. All rights reserved.
FAU - Zhang, Yafei
AU  - Zhang Y
AD  - Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, 
      USA. Electronic address: yafei.zhang@merck.com.
FAU - Yuan, Shuai Sammy
AU  - Yuan SS
AD  - Kite Pharma, A Gilead Company, Santa Monica, CA, USA.
FAU - Eagel, Barry A
AU  - Eagel BA
AD  - Clinical Research and Pharmacovigilance Consultant, West Orange, NJ, USA.
FAU - Li, Hal
AU  - Li H
AD  - Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, 
      USA.
FAU - Lin, Li-An
AU  - Lin LA
AD  - Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, 
      USA.
FAU - Wang, William W B
AU  - Wang WWB
AD  - Biostatistics and Research Decision Sciences, Merck & Co., Inc., Kenilworth, NJ, 
      USA.
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PT  - Research Support, Non-U.S. Gov't
DEP - 20201020
PL  - United States
TA  - Contemp Clin Trials
JT  - Contemporary clinical trials
JID - 101242342
SB  - IM
MH  - Bayes Theorem
MH  - Computer Simulation
MH  - Humans
MH  - *Research Design
OTO - NOTNLM
OT  - Bayesian hierarchical model
OT  - Drug safety
OT  - Meta-analysis
OT  - Pharmacovigilance
OT  - Randomized clinical trial
EDAT- 2020/10/23 06:00
MHDA- 2021/09/25 06:00
CRDT- 2020/10/22 20:12
PHST- 2020/06/19 00:00 [received]
PHST- 2020/09/18 00:00 [revised]
PHST- 2020/10/15 00:00 [accepted]
PHST- 2020/10/23 06:00 [pubmed]
PHST- 2021/09/25 06:00 [medline]
PHST- 2020/10/22 20:12 [entrez]
AID - S1551-7144(20)30261-5 [pii]
AID - 10.1016/j.cct.2020.106183 [doi]
PST - ppublish
SO  - Contemp Clin Trials. 2020 Dec;99:106183. doi: 10.1016/j.cct.2020.106183. Epub 
      2020 Oct 20.