the us food and drug administration ( fda ) adverse event reporting system ( faers , formerly aers ) is a database that contains information on adverse event and medication error reports submitted to the fda 1 - 3 . besides those from manufacturers
the faers structure adheres to the international safety reporting guidance issued by the international conference on harmonisation , ich e2b , and adverse events are coded to terms in the medical dictionary for regulatory activities ( meddra ) terminology 4 .
the original system was initiated in 1969 ; however , reporting markedly increased following the last major revision in 1997 5 , 6 . to date , the faers contains more than 4 million reports and is the largest repository of spontaneously reported adverse events in the world 5 , 6 .
the fda releases data to the general public , and this has allowed us to conduct pharmacoepidemiological studies and/or pharmacovigilance analyses .
data mining algorithms have been developed for the quantitative detection of signals 7 - 11 .
a signal indicates an association between a drug and an adverse event or drug - associated adverse event , including the proportional reporting ratio ( prr ) 12 , reporting odds ratio ( ror ) 13 , information component ( ic ) given by a bayesian confidence propagation neural network 14 , and empirical bayes geometric mean ( ebgm ) 15 .
associations with adverse events of interests were previously analyzed for 16 drugs using reports in the faers database between 2004 and 2009 16 - 22 . whether an adverse event is detected as a signal has been shown to depend on the algorithms ; however , of the 4 methods
, the ror method provided the highest number of signals , while the ebgm method provided the lowest 23 . in the present study , the commonality of prr- , ror- , ic- , and
data were retrieved from the public release of the faers database from the first quarter of 2004 through to the end of 2009 .
duplicated reports were deleted and arbitrary drug names were revised , resulting in a reduction in the number of reports from 2,231,029 to 1,644,220 .
signal scores , i.e. , the prr , ror , ic , and ebgm values , were calculated for 16 unrelated drugs to assess associations with adverse events , including 2 antimicrobials ( colistin and tigecycline ) , 4 hmg - coa reductase inhibitors ( statins ) ( pravastatin , simvastatin , atorvastatin , and rosuvastatin ) , 2 proton pump inhibitors ( ppis ) ( omeprazole and esomeprazole ) , warfarin , 2 antiplatelets ( aspirin and clopidogrel ) , and 5 anticancer agents ( cisplatin , carboplatin , oxaliplatin , 5-fluorouracil , and capecitabine ) .
it is noted that the associations of these drugs with adverse events have already been published 16 - 22 .
the unpaired student 's t - test / welch 's test or mann - whitney 's u test was used for two - group comparisons of the values .
figure 1 shows the relationship among the prr- , ror- , ic- , and ebgm - based signals , which was commonly observed for all 16 drugs .
all ebgm - based signals were included in the prr - based signals as well as ic- or ror - based ones .
the prr- and ic - based signals were included in the ror - based ones .
therefore , ror - based signals could be stratified into 5 groups ; signals detected by the ror only , signals detected by the ror and prr , signals detected by the ror and ic , signals detected by the ror , prr , and ic , and signals detected by the 4 methods . table 1 lists the numbers of signals in the 5 groups .
the ratio of the total number of ebgm - based signals to that of signals detected by the ror only varied from 3.9% with omeprazole to 57.3% with oxaliplatin .
the ratio of the total number of ebgm - based signals to that of ror - based signals varied from 1.7% with omeprazole to 20.5% with oxaliplatin .
table 2 lists the prr scores of prr - based signals . since prr - based signals could be divided into 2 groups based on whether adverse events were also detected as signals by the ebgm method ( figure 1 ) , the effects of additional detection by the ebgm method on prr scores was examined .
as shown in table 2 , the scores were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the ebgm method .
the effects of additional detection by the ebgm method found for prr scores were not observed for the ror , whereas the ic scores of ic - based signals were the same as the prr scores of prr - based signals .
several studies previously compared data mining algorithms 13 , 24 - 29 ; however , as bate and evans recently concluded 7 , different algorithms have slightly different properties such that one may consequently be preferable in a particular application .
if used for pharmacovigilance , data mining algorithms should be assessed from the standpoint of early and timely signal detection 30 - 33 .
recently compared the timing of early signal detection with prr , ror , ic , and ebgm using the faers database , and concluded that the ror performed better 30 .
we previously reported that the ror method provided the highest number of signals , while the ebgm method provided the lowest 23 .
the difference in the number of signals can be attributed to a higher rate of false positives or lower ability to detect signals . in the present study ,
the ebgm method was shown to be the most conservative among the 4 methods , which suggested that it was suitable for pharmacoepidemiological studies .
in contrast , the ror method was shown to be the most comprehensive , indicating its usefulness for pharmacovigilance .
these 4 data mining algorithms were used in our previous studies 16 - 22 , and adverse events were listed as drug - associated , when at least 1 of the 4 indices met the criteria . however , the results shown in figure 1 demonstrated that lists of adverse events were only identical when the ror method was applied , which suggested that care should be taken in interpreting data when signals are not detected by the ebgm method . based on the number of signals , 16 drugs could be classified into 4 groups .
group 1 included 2 antimicrobials , which were characterized by the lower number of signals .
the total number of co - occurrences with colistin was only 1,491 , and 1,906 for tigecycline .
these were markedly less than those of the other 14 drugs ; from 33,197 with oxaliplatin to 220,194 with atorvastatin .
the lower number of signals can be explained by comparatively infrequent use , and , therefore , a smaller number of reports in the database .
group 2 included 4 statins and 2 ppis characterized by a lower number of ebgm - based signals , and group 3 included warfarin and 2 antiplatelets by a higher number of ebgm - based signals .
group 4 included 5 anticancer agents characterized by a much higher number of ebgm - based signals .
the total number of ror - based signals was similar among drugs in groups 2 - 4 ; from 619 with rosuvastatin to 884 with cisplatin .
the ror method is feasible for detecting more signals , including false positives , than the ebgm method .
the difference observed in the ratio of ebgm - based to ror - based signals may reflect whether adverse events are generally found . a pilot study performed by hochberg et al . in 2009 concerning drug - versus - drug comparisons revealed that the rank - order of adverse event rates in the faers database was consistent with the results of published studies 34 , which encouraged the use of the database for comparisons .
in other investigations , the number of reports with or without normalization by usage or sales during the corresponding period was used to compare drugs 35 ; however , adverse events are underreported , which may lead to incorrect conclusions 36 - 38 .
signal scores have also been considered inappropriate for determining the rank - order of drugs in terms of risk ; however , few studies have been published to date . in the present study ,
the ebgm method was shown to be the most conservative among the 4 methods ; therefore , it is important to confirm whether this method can provide important information similar to that in well - organized clinical studies .
 objectives : data mining algorithms have been developed for the quantitative detection of drug - associated adverse events ( signals ) from a large database on spontaneously reported adverse events . in the present study , the commonality of signals detected by 4 commonly used data mining algorithms was examined.methods : a total of 2,231,029 reports were retrieved from the public release of the us food and drug administration adverse event reporting system database between 2004 and 2009 . 
 the deletion of duplicated submissions and revision of arbitrary drug names resulted in a reduction in the number of reports to 1,644,220 . 
 associations with adverse events were analyzed for 16 unrelated drugs , using the proportional reporting ratio ( prr ) , reporting odds ratio ( ror ) , information component ( ic ) , and empirical bayes geometric mean ( ebgm).results : all ebgm - based signals were included in the prr - based signals as well as ic- or ror - based ones , and prr- and ic - based signals were included in ror - based ones . 
 the prr scores of prr - based signals were significantly larger for 15 of 16 drugs when adverse events were also detected as signals by the ebgm method , as were the ic scores of ic - based signals for all drugs ; however , no such effect was observed in the ror scores of ror - based signals.conclusions : the ebgm method was the most conservative among the 4 methods examined , which suggested its better suitability for pharmacoepidemiological studies . 
 further examinations should be performed on the reproducibility of clinical observations , especially for ebgm - based signals . 