J Young Pharm, 2018; 10(4): 444-449
A multifaceted peer reviewed journal in the eld of Pharmacy
www.jyoungpharm.org | www.phcog.net
Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018 444
e US Food and Drug Administration (FDA) has maintained the
Adverse Events Reporting System (AERS) database since 1968 with the
intention of continuous monitoring of drugs during the post-marketing
surveillance.1 AERS database is a collection of suspected Adverse Event
(AEs) reports from pharmaceutical companies, consumers and health-
care professionals.2 To date, FDA AERS database contains more than
10 million AE reports and receive nearly half a million each year. Many
approaches have been adopted into post-marketing studies, including
prescription-event monitoring (PEM), spontaneous reporting, case
review, observational study and drug utilization review.
Compared to clinical trials and traditional epidemiologic studies, the
computer-assisted Data Mining Algorithms (DMAs) are relatively new
and characterized by providing a fast and ecient way of detecting
possible Adverse Drug Reactions (ADRs) signal.3 Several DMAs have
been well described in the literature, mainly including the Reporting
Odds Ratio (ROR),4 Multi-item Gamma Poisson Shrinker (MGPS),5 the
Proportional Reporting Ratio (PRR),6 and the Information Component
(IC).7 By integrating such computer-aided techniques, incorporating
statistical analyses and centralizing dierent data resources, it is not only
possible to minimize the human eorts and errors but also assist the
regulatory bodies and safety councils.8
A.M. Wilson9 dened ‘data mining’ as ‘the use of statistical techniques,
such as disproportionality measures for database or large information
sources for extracting an unknown information’. At present, three major
DMAs such as the PRR of the Netherlands, the ROR of the United Kingdom
and the IC of the WHO are widely used.9-10
Speaking of the DMAs, one of the frequently discussed and yet to be
resolved question is which algorithm has superior performance. e
absence of gold standard,11 enormous ADR reports, dierent coding
systems, a wide range of data mining processes and structural dissimi-
larities of databases made the comparisons across the DMAs dicult.
Limited studies have been conducted to compare the DMAs thus far.
is study aims to compare and appraise the performance of signal
detection techniques used in data mining.
Most commonly used three DMAs (ROR, PRR and IC) were selected
based on a literature survey. DMAs were applied retrospectively (Table 1
and 2) in US FDA AERS database to detect ve conrmed Drug Event
Combinations (DEC) (Table 3). e DEC was selected in regard to the
withdrawal of the drug from the market between 2006-2015 or the
change in labelling criteria or black box warning of the drug during the
time period of 2006-2015. e time period is important because the data
available in USFDA AERS database for signal detection is from 2006.
AE reports from the FDA AERS database were used for the study. It is a
surveillance program used for detecting serious AEs that have not been
identied during premarketing analysis.12
A Comparative Study of Data Mining Algorithms used for Signal
Detection in FDA AERS Database
Viswam Subeesh1,*, Eswaran Maheswari2, Ganesan Rajalekshmi Saraswathy2, Ann Mary Swaroop3, Satya Sai Minnikanti3
1Research Scholar, Department of Pharmacy Practice, Faculty of Pharmacy, M.S Ramaiah University of Applied Sciences, Bengaluru, Karnataka, INDIA.
2Professor, Department of Pharmacy Practice, Faculty of Pharmacy, M.S Ramaiah University of Applied Sciences, Bengaluru, Karnataka, INDIA.
3Associate Professor, Department of Pharmacy Practice, Faculty of Pharmacy, M.S Ramaiah University of Applied Sciences, Bengaluru, Karnataka, INDIA.
Objective: Signal detection is a technique in pharmacovigilance for the
early detection of new, rare reactions (desired or undesired) of a drug. This
study aims to compare and appraise the performance of data mining
algorithms used in signal detection. Method: Most commonly used three
data mining algorithms (DMAs) (Reporting Odds Ratio (ROR), Proportional
Reporting Ratio (PRR) and Information Component (IC)) were selected and
applied retrospectively in USFDA Adverse Event Reporting System database
to detect ve conrmed Drug Event Combinations. They were selected in
such a way that the drug is withdrawn from the market or label change
between 2006-2015. A value of ROR-1.96SE>1, PRR≥2, χ2>4 or IC- 2SD>0
were considered as the positive signal. The data mining algorithms were
compared for their sensitivity and early detection. Result: Among the three
data mining algorithms, Information Component was found to have a
maximum sensitivity (100%) followed by Reporting Odds Ratio (60%) and
Proportional Reporting Ratio (40%). Sensitivity associated with the number
of reports per drug event combination and early signal detection suggested
that information component needs comparatively fewer reports to show
positive signal than the other two data mining algorithms. ROR and PRR
showed comparable results. Conclusion: Early detection of a reaction is
possible using signal detection technique. Information component was
found to be sensitive method compared with other two data mining
algorithms in FDA Adverse Event Reporting System database. As the number
of reports of drug event combination increased, the sensitivity and compa-
rability of data mining algorithm also increased.
Key words: Signal Detection, Data mining algorithms, FDA AERS Database,
Disproportionality Analysis, Pharmacovigilance.
Mr. Subeesh K Viswam, Research Scholar, Department of Pharmacy Practice,
Faculty of Pharmacy, M.S Ramaiah University of Applied Sciences, Bengaluru,
This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others
to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.
Subeesh, et al.: A Comparative Study of Data Mining Algorithms
Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018 445
ROR, PRR and IC were applied on USFDA AERS database to detect ve
conrmed DEC and to compare the DMAs.
Propoxyphene was approved in 1957 and was withdrawn
from the market in 2010 owing to serious cardiac toxicity. A total of
366 DEC were reported from 2005Q1 to 2010Q4 in USFDA AERS
database (Table 4). ROR, 1.43 (95% CI, 1.26-1.52) and PRR, 2.8 (95% CI,
2.24- 3.32) showed positive signal from 2009Q1 and IC, 0.62 (95% CI,
0.06 -1.02) showed positive signal from 2007Q1 for the selected DEC.
PTs used for data mining were “cardiac arrest” and “cardio-respiratory
arrest”. Positive signals were highlighted with bold letters.
Sibutramine was approved in 1997 as an oral anorexiant. Later in
2010, it was withdrawn from the market due to severe cardio and
cerebrovascular accidents and associated death. A total of 4182 DEC
were reported from 2005Q1 to 2010Q4 in USFDA AERS database
(Table 4). Only IC, 0.28 (95% CI, 0.05 – 0.54) showed a positive signal
for sibutramine from 2009Q1 whereas ROR and PRR failed to show a
positive signal for the given DEC in the specied duration. PT used for
data mining were “cerebrovascular disease” and “stroke”. Positive signals
were highlighted with bold letters.
Rosiglitazone was approved in 1999 for diabetes mellitus and was with-
drawn from the market because of cardiac toxicity and associated death
in 2010. A total of 11839 DEC were reported from 2005Q1 to 2010Q4
in USFDA AERS database (Table 4). ROR, 1.4 (95% CI, 1-1.8) showed
positive signal from 2009Q3, PRR, 2.7 (95% CI, 2.1-3.6) from 2009Q4
and IC, 0.24 (95% CI, 0.02 - 0.5) showed positive signal from 2008Q3
for the given DEC in the specied period. PT used for data mining were
“Myocardial Infarction” (MI). Positive signals were highlighted with
Sitagliptin was approved in 2006 for diabetes mellitus. Recently (2012),
the labelling criteria of sitagliptin had changed to include a black box
warning of renal failure associated with sitagliptin. A total of 854 DEC
were reported from 2005Q1 to 2015Q2 in USFDA AERS database (Table
5). Only IC, 0.1 (95% CI, 0 – 0.23) showed positive signal (from 2011Q2)
whereas ROR and PRR failed to show positive signal. PT used for data
mining were “renal failure acute”, “creatinine renal clearance decreased”
and “blood creatinine increases”. Positive signals were highlighted with
Canagliozin is approved in 2013 for the treatment of Diabetes Mel-
litus and in 2015 it was subjected to a change in labelling criteria to
incorporate safety warning of Urinary Tract Infection (UTI). A to-
tal of 566 DEC were reported from 2013Q2 to 2015Q4 in USFDA
AERS database (Table 5). IC, 0.78 (95% CI, 0.53- 0.98) and ROR, 1.42
(95% CI, 1.11-1.94) showed a positive signal at 2015Q3 and 2015Q4
respectively. PT used for data mining were “urinary tract infection”.
Positive signals were highlighted with bold letters.
Sensitivity of DMAs
e sensitivity of three DMAs were assessed (Table 6) in accordance with
its potential to identify the signals prior to the withdrawal date. Out of
Table 1: The 2 × 2 table for the calculation of the signal.
Drug of Interest Other Drugs
ADR of interest A B
Other ADR C D
A: e number of reports containing both suspected drug and suspected ADRs
B: e number of reports containing drug of choice but with other ADRs
C: e number of reports containing the event of interest but with other medications
D: e number of reports concerning other medications and other ADRs
Table 2: Formula for the computation of signal.
Serial No. Measures Computation Threshold
1ROR ROR= (A/B)/(C/D)
≥ 3 cases reported
p(x) = Probability of a suspected drug being reported in a case report; p(y) =
Probability of a suspected event being reported in a case report; p(x, y) = Probability
that suspected drug and event simultaneously being reported in a case report
Table 3: Drug Event Combinations.
Sl. No. Drug Event Present status
1 Propoxyphene Serious toxicity to the
2 Sibutramine Cardio and
outcomes and death
3 Rosiglitazone Serious cardiac toxicity Withdraw from
4Sitagliptin Renal failure Change in label
5 Canaglifozin Urinary Tract Infection Change in label
Source: FDA annual report
FDA AERS database downloaded from USFDA ocial website. e
database converted into excel from text format for the ease of anal-
ysis and computation. Primary suspects and secondary suspect
case IDs of the drug of interest were noted from DRUG le. AE pertaining
to those case IDs were ltered and listed. Dierent parameters of the
DMA equation (Table 1) were computed and nally applied to the equation
e threshold was predened as PRR of ≥2.0 with a Chi-squared test
of ≥4.0, at least three reports (n ≥3) of that preferred term(PT), IC with
IC-2SD>0 and ROR with ROR-1.96SE>1. e condence interval (95%)
were considered to be statistically signicant.13
Subeesh, et al.: A Comparative Study of Data Mining Algorithms
446 Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018
tively fewer reports to show positive signal than ROR and PRR. Parallel
to the above result, ROR and PRR are almost identical in their sensitivity.
In this study, the most commonly used three DMAs are ROR, PRR and
IC which are applied retrospectively in USFDA AERS database to detect
ve known and conrmed ADRs associated with the drug withdrawal
or change in labelling criteria. Further comparisons across the selected
DMAs were executed to identify the sensitivity by means of early detection
and number of reports.
Hitherto, there is no clearly dened method to compare the DMAs with
respect to their sensitivity or performance. e major drawback in
comparing the DMAs is the lack of golden standards.14 In E.P. van
Puijenbroek, A. Bate11 study, they compared the DMAs like PRR, Yule’s
Q and Chi-square with IC, which was considered as a golden standard by
them. Another study conducted by K. Kubota15 in Japanese spontaneous
reports, compared ve DMAs. According to Kubota et al., the number of
ve, IC showed maximum sensitivity (100%) followed by ROR (60%)
and PRR (40%).
The sensitivity of DMA based on Early Detection
DMAs were assessed for their ability for early detection of a signal
(Table 7). Index Date of Withdrawn/Label change (IDW/L) were identied
from literature or ocial websites of USFDA. e quadrant from which
DEC started showing positive signals were allocated as Index Date of
Detection (IDD). Early detection was quantied by IDD subtracted from
IDW/L. IC was found to be the most sensitive, as it detects positive signal
well before other DMAs and there is no remarkable dierence in sensi-
tivity of early detection between ROR and PRR.
Sensitivity associated with the number of reports per DEC
DMAs were assessed for their sensitivity with respect to the number of
reports required to show positive signal (Figure 1). Sensitivity associated
with the number of reports per DEC suggested that IC need compara-
Table 4: DMA of propoxyphene and reporting of cardiac arrest, Sibutramine and reporting of cerebrovascular disease and Rosiglitazone and reporting
Propoxyphene and reporting of cardiac
Sibutramine and reporting of
Rosiglitazone and reporting of MI
Time Period ROR-1.96SE PRR-1.96SE IC-2SD ROR-1.96SE PRR-1.96SE IC-2SD ROR-1.96SE PRR-1.96SE IC-2SD
2005Q1 -1.72 -0.69 -2.83 -6.64 -2.16 -1.15 -1.86
2005Q2 -2.03 -1.03 -2.86 -4.89 -4.89 -3.83 -2.69 -1.6 -1.81
2005Q3 -0.87 0.13 -1.63 -4.38 -4.38 -2.7 -2.95 -1.84 -1.39
2005Q4 0.42 1.41 -0.27 -3.81 -3.81 -1.91 -2.87 -1.75 -1.13
2006Q1 0.4 1.39 -0.26 -3.74 -3.74 -1.71 -2.97 -1.83 -1.02
2006Q2 0.42 1.42 -0.18 -4.01 -4.01 -1.65 -2.79 -1.67 -0.87
2006Q3 0.41 1.4 -0.18 -3.97 -3.97 -1.52 -2.78 -1.78 -0.83
2006Q4 0.53 1.53 -0.05 -3.97 -3.97 -1.52 -2.64 -1.53 -0.76
2007Q1 0.66 1.65 0.06 -3.97 -3.97 -1.52 -2.53 -1.53 -0.73
2007Q2 0.63 1.62 0.04 -3.33 -3.14 -1.09 -1.71 -0.66 -0.43
2007Q3 0.65 1.64 0.05 -2.92 -2.79 -0.87 -1.25 -0.22 -0.27
2007Q4 0.64 1.63 0.05 -2.53 -2.53 -0.74 -0.99 0.06 -0.19
2008Q1 0.91 1.89 0.35 -2.42 -2.33 -0.68 -0.76 0.28 -0.13
2008Q2 0.94 1.92 0.38 -2.15 -2.08 -0.59 -0.39 0.63 -0.01
2008Q3 0.94 1.93 0.39 -2.08 -2.08 -0.58 -0.29 0.72 0.02
2008Q4 0.97 1.96 0.42 -0.32 -0.31 -0.02 0.18 1.17 0.21
2009Q1 1.26 2.23 0.78 -0.18 -0.17 0.05 0.64 1.6 0.48
2009Q2 1.25 2.22 0.77 -0.12 -0.11 0.07 0.73 1.69 0.55
2009Q3 1.24 2.22 0.77 0.1 0.1 0.18 1 1.94 0.78
2009Q4 1.26 2.24 0.79 0.31 0.3 0.28 1.17 2.1 0.96
2010Q1 1.4 2.37 1 0.4 0.39 0.34 1.27 2.19 1.07
2010Q2 1.38 2.35 0.97 0.58 0.56 0.45 1.4 2.31 1.24
2010Q3 1.4 2.37 1 0.77 0.75 0.6 1.59 2.48 1.54
2010Q4 1.43 2.4 1.04 0.86 0.83 0.66 1.69 2.57 1.7
ROR=Reporting Odds Ratio; PRR= Proportional Reporting Ratio; IC= Information Component; SD=Standard Deviation; SE= Standard Error. Bold Letters: Positive
Subeesh, et al.: A Comparative Study of Data Mining Algorithms
Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018 447
Table 6: The DEC detected by the three DMAs.
Sl. no Drug Event Combination ROR PRR IC
1 Propoxyphene and cardiac arrest √ √ √
2 Sibutramine and cerebrovascular disorders X X √
3 Rosiglitazone and MI √ √ √
4 Sitagliptin and renal failure X X √
5 Canagliozin and UTI √ X √
Sensitivity 60% 40% 100%
‘√’ means that the DMA could identify the signals prior to the withdrawal date;
‘X’ means that algorithms did not identify the signals prior to the withdrawal date.
Table 7: Sensitivity of each DMA in terms of early detection.
Sl. No. Drug Event IDW IDD Status
1 Propoxyphene Cardiac Arrest 2010Q4 2009Q1 3 2009Q1 3 2007Q1 14 2010 Withdraw
2 Sibutramine Cerebrovascular
2010Q4 ___ ___ ___ ___ 2009Q1 7 2010 Withdraw
3 Rosiglitazone MI 2010Q4 2009Q3 5 2009Q4 4 2008Q3 9 2010 Withdraw
4Sitagliptin Renal failure 2012Q4 ___ ___ ___ ___ 2011Q2 4 2012 Change in
5 Canagliozin Urinary Tract
2016Q2 2015Q4 1 ___ ___ 2015Q3 2 2016 Change in
Abbreviations: ROR = Reporting Odds Ratio; PRR = Proportional Reporting Ratio; IC = Information Component; IDW = Index Date of Withdrawn; IDD = Index Date of
Table 5: Sitagliptin and reporting of renal failure and Canagliozin and
reporting of UTI.
Sitagliptin and reporting of
Canagliozin and reporting
2006Q4 -0.13 0.89 -2.84 - - -
2007Q1 0.09 1.1 -1.22 - - -
2007Q2 -0.37 0.64 -0.74 - - -
2007Q3 -0.3 0.7 -0.49 - - -
2007Q4 -0.2 0.8 -0.34 - - -
2008Q1 -0.18 0.82 -0.28 - - -
2008Q2 -0.13 0.87 -0.27 - - -
2008Q3 -0.22 0.78 -0.26 - - -
2008Q4 -0.26 0.74 -0.23 - - -
2009Q1 -0.25 0.75 -0.2 - - -
2009Q2 -0.25 0.76 -0.17 - - -
2009Q3 -0.26 0.75 -0.16 - - -
2009Q4 -0.22 0.79 -0.13 - - -
2010Q1 -0.17 0.83 -0.1 - - -
2010Q2 -0.17 0.83 -0.08 - - -
2010Q3 -0.15 0.86 -0.07 - - -
2010Q4 -0.14 0.86 -0.06 - - -
2011Q1 -0.09 0.91 -0.03 - - -
2011Q2 -0.05 0.96 0 - - -
2011Q3 0.01 1.01 0.03 - - -
2011Q4 0 0.99 0.03 - - -
2012Q1 0 1 0.04 - - -
2012Q2 -0.01 0.99 0.04 - - -
2012Q3 -0.01 1 0.05 - - -
2012Q4 0.01 1.01 0.06 - - -
2013Q1 -0.01 0.99 0.05 - - -
2013Q2 -0.01 0.98 0.06 -1.39 -0.36 -4.4
2013Q3 -0.03 0.97 0.06 0.08 3.63 -1.81
2013Q4 -0.05 0.95 0.06 0.18 4.14 -1.04
2014Q1 -0.05 0.95 0.06 0.2 3.09 -0.87
2014Q2 -0.1 0.9 0.05 -0.21 0.8 -0.84
2014Q3 -0.16 0.85 0.04 -0.18 0.82 -0.62
2014Q4 -0.18 0.83 0.04 -0.06 0.96 -0.49
2015Q1 -0.17 0.83 0.04 0.41 1.39 -0.17
2015Q2 -0.22 0.78 0.03 -0.55 0.48 -0.15
2015Q3 - - - 0.9 1.83 0.53
2015Q4 - - - 1.11 1.97 0.76
ROR=Reporting Odds Ratio; PRR= Proportional Reporting Ratio; IC= Informa-
tion Component; SD=Standard Deviation; SE= Standard Error. Bold Letters: Posi-
Subeesh, et al.: A Comparative Study of Data Mining Algorithms
448 Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018
DECs identied as signals were considered as the measure of sensitivity.
Compared to the above two studies,11,15 the ndings of the present study
seemed to be more reliable because the reference standard considered
is more robust. Comparison of DMAs is possible in many ways, but the
main aspect which directly inuences the DMA values is the number of
reports. us, according to this study, a cumulative number of reports
should be considered as the measure of sensitivity. Early detection of
ADRs is the main advantage of DMAs,16-17 therefore it can be considered
as a measure of sensitivity.
is study indicates that IC is more sensitive in terms of early detection
as well as the number of reports. However, among the DMAs, the sensi-
tivity dierence is statistically not signicant. Moreover, the sensitivity of
DMAs may vary if a dierent database is used hence, we cannot conclude
that IC is the most sensitive DMA. Nevertheless, for a given set of data
and DMAs, IC showed more sensitivity than other DMAs. It has been
observed that the sensitivity is proportional to the number of reports.
According to V.G. Koutkias and M.-C. Jaulent,18 the number of reports is
an important factor for signal strength.
According to the literature review, it was observed that no study has been
attempted to compare DMA to identify the sensitive method among the
existing algorithms. We comply with P. Waller19 study, as the selection
of DMAs should be done on the basis of their specicity, sensitivity and
predictive value in addition to a factor observed from our study. Early
detection of ADR plays a vital role which may reduce the casualties and
provide sucient time for a regulatory decision.
It is not surprising that IDD is earlier than IDW because once the positive
signal is identied, FDA requires adequate time to evaluate the situation,
assess the risk-benet prole of the drug and for a regulatory decision.
However, the use of DMAs could trigger the initiation of this process
earlier by recognizing signals in advance. Few studies have compared the
traditional method of ADR detection with computer-based signal detec-
tion techniques. A study conducted by A.W. I20 concluded that DMAs
detected safety signals well before the conventional ways. According to
D.J. Graham,21 there were 88,000–140,000 excess of cardiac disorders
associated with rofecoxib. It could have been reduced if the safety signal
was detected earlier.
We rely on the progression between the early detection of an ADR signal
to the nal decision that drug withdrawal or labelling change could more
likely be acquired due to the earlier detection of ADR signals as a
result of the applications of DMAs. Consequently, the time when the
FDA makes a decision will correspondingly occur earlier.
Limitations of the study
ere are some concerns regarding FDA AERS database. Under-reporting
is the main concern with regards to any spontaneous reporting system
(SRS) database. SRS will not reect the actual picture of the scenario.
us, more oen the situation was underestimated. Secondly, the reporting
may get biased when there is a change in labelling criteria or any special
updates regarding an ADR of the drug. As a result, over-reporting of
that particular ADR will occur which will decrease the signal strength
of other ADRs of the same drug (change in Ni value will aect the signal
e selection of brand names for data mining is another limitation which
we had come across. FDA AERS database is a collection of ADR reports
around the world but the main contributor is United States (US). e
brand names used for data mining in this study mainly focused on the
US, European countries and India. us, the chances of missing data
cannot be ruled out.
e aim of the study was to compare and appraise the performance
of signal detection techniques used in data mining. It is the rst study
attempted to address the importance of early detection of ADR and iden-
tication of the sensitive method. Even though there is no statistically
signicant dierence among three DMAs, IC was found to be sensitive
method compared with other two DMAs in FDA AERs database. e
sensitivity and comparability of DMA is proportionate to the number of
reports of DEC.
CONFLICT OF INTEREST
e authors declare no conict of interest.
FDA: Food and Drug Administration; AERS: Adverse Events Report-
ing System: AEs: Adverse Event; PEM: Prescription-Event Monitoring;
DMAs: Data Mining Algorithms; ADRs: Adverse Drug Reactions; ROR
: Reporting Odds Ratio; MGPS: Multi-item Gamma Poisson Shrinker;
PRR: Proportional Reporting Ratio; IC: Information Component;
DEC: Drug Event Combinations; IDW/L: Index Date of Withdrawn/
1. Harpaz R, DuMouchel W, LePendu P, Bauer MA, Ryan P, Shah NH. Performance
of pharmacovigilance signal-detection algorithms for the fda adverse event
reporting system. Clin Pharmacol Ther. 2013;93(6):539-46. 10.1038/clpt.2013.24
2. Bie SD, Ferrajolo C, Straus SM, Verhamme KM, Bonhoeffer J, Wong IC, et al.
Pediatric drug safety surveillance in fda-aers: A description of adverse
events from grip project. PLoS One. 2015;10(6):e0130399. 10.1371/journal.
3. Rossi AC, Knapp DE, Anello C, et al. Discovery of adverse drug reactions: A
comparison of selected phase iv studies with spontaneous reporting methods.
JAMA. 1983;249(16):2226-8. 10.1001/jama.1983.03330400072029
4. Rothman KJ, Lanes S, Sacks ST. The reporting odds ratio and its advantages
over the proportional reporting ratio. Pharmacoepidemiology and Drug Saf.
5. Napoli AA, Wood JJ, Coumbis JJ, Soitkar AM, Seekins DW, Tilson HH. No
evident association between efavirenz use and suicidality was identied
from a disproportionality analysis using the faers database. J Int AIDS Soc.
6. Evans SJ, Waller PC, Davis S. Use of proportional reporting ratios (prrs) for
signal generation from spontaneous adverse drug reaction reports. Pharmaco-
epidemiol Drug Saf. 2001;10(6):483-6. 10.1002/pds.677
7. Bate A, Evans SJ. Quantitative signal detection using spontaneous adr reporting.
Pharmacoepidemiol Drug Saf. 2009;18(6):427-36. 10.10 02/pds.1742
8. Bate A, Lindquist M, Edwards IR, Orre R. A data mining approach for signal
detection and analysis. Drug Saf. 2002;25(6):393-7.
9. Wilson AM, Thabane L, Holbrook A. Application of data mining techniques in
Figure 1: Sensitivity associated with the number of reports per DEC.
Abbreviations: ROR = Reporting Odds Ratio; PRR = Proportional Reporting
Ratio; IC = Information Component.
Subeesh, et al.: A Comparative Study of Data Mining Algorithms
Journal of Young Pharmacists, Vol 10, Issue 4, Oct-Dec, 2018 449
Indian J Pharmacol. 2015; 47(3):241-2. 10.4103/0253-7613.157102
1 7. Hauben M, Reich L. Potential utility of data-mining algorithms for early detection
of potentially fatal/disabling adverse drug reactions: A retrospective evaluation.
J Clin Pharmacol. 2005;45(4):378-84. 10.1177/0091270004273936
18. Koutkias VG, Jaulent MC. Computational approaches for pharmacovigilance
signal detection: Toward integrated and semantically-enriched frameworks.
Drug Saf. 2015;38(3):219-32. 10.10 07/s40264-015-0278-8
19. Waller P, Puijenbroek EV, Egberts A, Evans S. The reporting odds ratio versus
the proportional reporting ratio:‘Deuce’. Pharmacoepid and drug saf. 2004;
20. I AW, Pratt NL, Kalisch LM, Roughead EE. Comparing time to adverse drug
reaction signals in a spontaneous reporting database and a claims database: A
case study of rofecoxib-induced myocardial infarction and rosiglitazone-induced
heart failure signals in australia. Drug Saf. 2014;37(1):53-64. 10.1007/s40264-
21. Graham DJ, Campen D, Hui R, Spence M, Cheetham C, Levy G, et al. Risk of
acute myocardial infarction and sudden cardiac death in patients treated with
cyclo-oxygenase 2 selective and non-selective non-steroidal anti-inammatory
drugs: Nested case-control study. Lancet. 2005;365(9458):475-81. 10.1016/
pharmacovigilance. Br J Clin Pharmacol. 2004;57(2):127-34. 10.1046/j.1365-
10. Szarfman A, Machado SG, O’Neill RT. Use of screening algorithms and computer
systems to efciently signal higher-than-expected combinations of drugs and
events in the us fda’s spontaneous reports database. Drug Saf. 2002;25(6):381-92.
11. Puijenbroek EPV, Bate A, Leufkens HG, Lindquist M, Orre R, Egberts AC.
A comparison of measures of disproportionality for signal detection in sponta-
neous reporting systems for adverse drug reactions. Pharmacoepidemiol Drug
Saf. 2002;11(1):3-10. 10.1002/pds.668
12. Shamloo B, Chhabra P, Freedman A, Potosky A, Malin J, Smith S. Novel adverse
events of bevacizumab in the us fda adverse event reporting system database.
Drug Saf. 2012;35(6):507-18. 10.2165/11597600-000000000-000 00
13. Deshpande G, Gogolak V, Smith S. Data mining in drug safety. Pharmaceut
Med. 2010;24(1):37-43. 10.1007/BF03256796
14. Lindquist M, Stahl M, Bate A, Edwards IR, Meyboom RH. A retrospective evalu-
ation of a data mining approach to aid nding new adverse drug reaction signals
in the who international database. Drug Saf. 2000;23(6):533-42.
15. Kubota K, Koide D, Hirai T. Comparison of data mining methodologies using
japanese spontaneous reports. Pharmacoepidemiol Drug Saf. 2004;13(6):387-94.
16. Chakraborty BS. Pharmacovigilance: A data mining approach to signal detection.
Article History: Submission Date : 19-05-2018; Revised Date : 30-06-2018; Acceptance Date : 06-08-2018.
Cite this article: Subeesh V, Maheswari E, Saraswathy GR, Swaroop AM, Minnikanti SS. A Comparative Study of Data Mining Algorithms used for Signal
Detection in FDA AERS Database. J Young Pharm. 2018;10(4):444-9.