PMID- 34453158
OWN - NLM
STAT- MEDLINE
DCOM- 20220309
LR  - 20220309
IS  - 1477-4054 (Electronic)
IS  - 1467-5463 (Linking)
VI  - 22
IP  - 6
DP  - 2021 Nov 5
TI  - Detection algorithms and attentive points of safety signal using spontaneous 
      reporting systems as a clinical data source.
LID - bbab347 [pii]
LID - 10.1093/bib/bbab347 [doi]
AB  - Continuous evaluation of drug safety is needed following approval to determine 
      adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous 
      reporting systems are an important source of information for the detection of AEs 
      not identified in clinical trials and for safety assessments that reflect the 
      real-world use of drugs in specific populations and clinical settings. The use of 
      spontaneous reporting systems is expected to detect drug-related AEs early after 
      the launch of a new drug. Spontaneous reporting systems do not contain data on 
      the total number of patients that use a drug; therefore, signal detection by 
      disproportionality analysis, focusing on differences in the ratio of AE reports, 
      is frequently used. In recent years, new analyses have been devised, including 
      signal detection methods focused on the difference in the time to onset of an AE, 
      methods that consider the patient background and those that identify drug-drug 
      interactions. However, unlike commonly used statistics, the results of these 
      analyses are open to misinterpretation if the method and the characteristics of 
      the spontaneous reporting system cannot be evaluated properly. Therefore, this 
      review describes signal detection using data mining, considering traditional 
      methods and the latest knowledge, and their limitations.
CI  - (c) The Author(s) 2021. Published by Oxford University Press. All rights reserved. 
      For Permissions, please email: journals.permissions@oup.com.
FAU - Noguchi, Yoshihiro
AU  - Noguchi Y
AUID- ORCID: 0000-0002-9110-9604
AD  - Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, 
      Daigakunishi, Gifu 501-1196, Japan.
FAU - Tachi, Tomoya
AU  - Tachi T
AD  - Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, 
      Daigakunishi, Gifu 501-1196, Japan.
FAU - Teramachi, Hitomi
AU  - Teramachi H
AD  - Laboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4, 
      Daigakunishi, Gifu 501-1196, Japan.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Brief Bioinform
JT  - Briefings in bioinformatics
JID - 100912837
SB  - IM
MH  - *Adverse Drug Reaction Reporting Systems
MH  - *Algorithms
MH  - Bayes Theorem
MH  - Data Mining
MH  - Databases, Factual
MH  - Drug-Related Side Effects and Adverse Reactions/*diagnosis/epidemiology
MH  - Humans
MH  - Medical Informatics/*methods
MH  - Models, Statistical
MH  - Odds Ratio
MH  - ROC Curve
MH  - Reproducibility of Results
OTO - NOTNLM
OT  - disproportionality analysis
OT  - signal detection
OT  - spontaneous reporting systems
OT  - time to onset algorithm
EDAT- 2021/08/29 06:00
MHDA- 2022/03/11 06:00
CRDT- 2021/08/28 05:55
PHST- 2021/05/14 00:00 [received]
PHST- 2021/07/30 00:00 [revised]
PHST- 2021/08/02 00:00 [accepted]
PHST- 2021/08/29 06:00 [pubmed]
PHST- 2022/03/11 06:00 [medline]
PHST- 2021/08/28 05:55 [entrez]
AID - 6358402 [pii]
AID - 10.1093/bib/bbab347 [doi]
PST - ppublish
SO  - Brief Bioinform. 2021 Nov 5;22(6):bbab347. doi: 10.1093/bib/bbab347.