PMID- 38515248
OWN - NLM
STAT- Publisher
LR  - 20240322
IS  - 1520-5711 (Electronic)
IS  - 1054-3406 (Linking)
DP  - 2024 Mar 21
TI  - Bayesian spatial cluster signal learning with application to adverse event (AE).
PG  - 1-13
LID - 10.1080/10543406.2024.2325148 [doi]
AB  - There is growing interest in understanding geographic patterns of medical 
      device-related adverse events (AEs). A spatial scan method combined with the 
      likelihood ratio test (LRT) for spatial-cluster signal detection over the 
      geographical region is universally used. The spatial scan method used a moving 
      window to scan the entire study region and collected some candidate sub-regions 
      from which the spatial-cluster signal(s) will be found. However, it has some 
      challenges, especially in computation. First, the computational cost increased 
      when the number of sub-regions increased. Second, the computational cost may 
      increase if a large spatial-cluster pattern is present and a flexible-shaped 
      window is used. To reduce the computational cost, we propose a Bayesian 
      nonparametric method that combines the ideas of Markov random field (MRF) to 
      leverage geographical information to find potential signal clusters. Then, the 
      LRT is applied for the detection of spatial cluster signals. The proposed method 
      provides an ability to capture both locally spatially contiguous clusters and 
      globally discontiguous clusters, and is manifested to be effective and tractable 
      using hypothetical Left Ventricular Assist Device (LVAD) data as an illustration.
FAU - Yang, Hou-Cheng
AU  - Yang HC
AD  - Center for Spatial Temporal Modeling for Applications in Population Sciences, 
      Department of Biostatistics and Data Science, The University of Texas Health 
      Science Center at Houston, Houston, United States.
FAU - Hu, Guanyu
AU  - Hu G
AD  - Center for Spatial Temporal Modeling for Applications in Population Sciences, 
      Department of Biostatistics and Data Science, The University of Texas Health 
      Science Center at Houston, Houston, United States.
LA  - eng
PT  - Journal Article
DEP - 20240321
PL  - England
TA  - J Biopharm Stat
JT  - Journal of biopharmaceutical statistics
JID - 9200436
SB  - IM
OTO - NOTNLM
OT  - Bayesian nonparametric
OT  - Markov random field (MRF)
OT  - Mixture of finite mixtures
OT  - likelihood ratio test
OT  - medical device data
OT  - spatial-Cluster Signal
EDAT- 2024/03/22 06:45
MHDA- 2024/03/22 06:45
CRDT- 2024/03/22 02:03
PHST- 2024/03/22 06:45 [medline]
PHST- 2024/03/22 06:45 [pubmed]
PHST- 2024/03/22 02:03 [entrez]
AID - 10.1080/10543406.2024.2325148 [doi]
PST - aheadofprint
SO  - J Biopharm Stat. 2024 Mar 21:1-13. doi: 10.1080/10543406.2024.2325148.