PMID- 30324671
OWN - NLM
STAT- MEDLINE
DCOM- 20190923
LR  - 20191210
IS  - 1099-1557 (Electronic)
IS  - 1053-8569 (Linking)
VI  - 27
IP  - 12
DP  - 2018 Dec
TI  - Significance of data mining in routine signal detection: Analysis based on the 
      safety signals identified by the FDA.
PG  - 1402-1408
LID - 10.1002/pds.4672 [doi]
AB  - PURPOSE: Data mining has been introduced as one of the most useful methods for 
      signal detection by spontaneous reports, but data mining is not always effective 
      in detecting all safety issues. To investigate appropriate situations in which 
      data mining is effective in routine signal detection activities, we analyzed the 
      characteristics of signals that the US Food and Drug Administration (FDA) 
      identified from the FDA Adverse Event Reporting System (FAERS). METHODS: Among 
      the signals that the FDA identified from the FAERS between 2008 1Q and 2014 4Q, 
      we selected 233 signals to evaluate in this study. We conducted a 
      disproportionality analysis and classified these signals into two groups 
      according to the presence or absence of statistical significance in the reporting 
      odds ratio (ROR). Then, we compared the two groups based on the characteristics 
      of the suspected drugs and adverse events (AEs). RESULTS: Safety signals were 
      most frequently identified for new drugs that had been on the market for less 
      than 5 years, but some signals were still identified for old drugs (>/=20 years), 
      and most of them were statistically significant. The proportion of the signals 
      for "serious" events was significantly higher in the group of nonsignals by ROR 
      (Fisher's exact test, P = 0.032). CONCLUSIONS: Data mining was shown to be 
      effective in the following situations: (1) early detection of safety issues for 
      newly marketed drugs, (2) continuous monitoring of safety issues for old drugs, 
      and (3) signal detection of nonserious AEs, to which little attention is usually 
      given.
CI  - (c) 2018 John Wiley & Sons, Ltd.
FAU - Fukazawa, Chisato
AU  - Fukazawa C
AUID- ORCID: 0000-0001-8395-515X
AD  - Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of 
      Pharmaceutical Sciences, Kitasato University, Tokyo, Japan.
AD  - Japan Pharmaceutical Information Center, Tokyo, Japan.
FAU - Hinomura, Yasushi
AU  - Hinomura Y
AD  - Japan Pharmaceutical Information Center, Tokyo, Japan.
FAU - Kaneko, Masayuki
AU  - Kaneko M
AD  - Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of 
      Pharmaceutical Sciences, Kitasato University, Tokyo, Japan.
FAU - Narukawa, Mamoru
AU  - Narukawa M
AD  - Department of Clinical Medicine (Pharmaceutical Medicine), Graduate School of 
      Pharmaceutical Sciences, Kitasato University, Tokyo, Japan.
LA  - eng
PT  - Evaluation Study
PT  - Journal Article
DEP - 20181015
PL  - England
TA  - Pharmacoepidemiol Drug Saf
JT  - Pharmacoepidemiology and drug safety
JID - 9208369
SB  - IM
MH  - Adverse Drug Reaction Reporting Systems/*statistics & numerical data
MH  - Data Interpretation, Statistical
MH  - *Data Mining
MH  - Databases, Factual/statistics & numerical data
MH  - Drug-Related Side Effects and Adverse Reactions/*epidemiology
MH  - Humans
MH  - Odds Ratio
MH  - Pharmacoepidemiology/*methods/statistics & numerical data
MH  - United States
MH  - United States Food and Drug Administration/*statistics & numerical data
OTO - NOTNLM
OT  - data mining
OT  - pharmacoepidemiology
OT  - routine pharmacovigilance
OT  - signal detection
OT  - spontaneous reports
EDAT- 2018/10/17 06:00
MHDA- 2019/09/24 06:00
CRDT- 2018/10/17 06:00
PHST- 2018/01/23 00:00 [received]
PHST- 2018/08/31 00:00 [revised]
PHST- 2018/09/12 00:00 [accepted]
PHST- 2018/10/17 06:00 [pubmed]
PHST- 2019/09/24 06:00 [medline]
PHST- 2018/10/17 06:00 [entrez]
AID - 10.1002/pds.4672 [doi]
PST - ppublish
SO  - Pharmacoepidemiol Drug Saf. 2018 Dec;27(12):1402-1408. doi: 10.1002/pds.4672. 
      Epub 2018 Oct 15.