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260 Cobert’s Manual of Drug Safety and Pharmacovigilance
of the reporting rate include the use of generic products
that may not be included in the denominator, but for
which reported AEs are included in the numerator, as
well as counterfeit drugs where the denominator and
numerator are compromised. Despite these limitations,
drug utilization data can be stratified by gender, age,
and other demographic characteristics. Trends over
time in usage can be observed.
In summary, as Dr. David Goldsmith has said “The
numerator is bad, the denominator is worse, and the
ratio is meaningless.” Hence, one cannot calculate quan-
titative measures of risk for a particular AE based on
spontaneous data, only reporting frequencies. Period.
Quantitative Signal
Detection Methods
Although limited in how the data can be interpreted,
the large repositories of spontaneous reporting data
such as the FDA’s FAERS, WHO’s VigiBase, and the EU’s
EVDAS, can be used to monitor certain unexpected
patterns of reporting through innovative quantitative
techniques looking at proportional reporting. Referred
to as signal detection, these metrics include the pro-
portional reporting ratio (PRR), the Gamma Poisson
Shrinker (GPS), urn-model algorithm, reporting odds
ratio (ROR), Bayesian confidence propagation neural
network–information component (BCPNN-IC), and
adjusted residual score (ARS), sequential probability
ratio test (max-SPRT). The PRR is used as an example
of the application of quantitative disproportionality
method and is described below.
9
9
On these methods, the following references can be looked up:
(a) Evans SJ, Waller PC, Davis S, Use of proportional reporting ratios
(PRRs) for signal generation from spontaneous adverse drug reac-
tion reports, Pharmacoepidemiology Drug Safety 2001; 10(6): 483–
486. (b) DuMouchel W, Bayesian data mining in large frequency
tables, with an application to the FDA spontaneous reporting system
(with discussion), Am Stat. 1999; 53(3): 177–222. (c) van Puijen-
broek E, Diemont W, van Grootheest K, Application of quantita-
tive signal detection in the Dutch spontaneous reporting system for
adverse drug reactions, Drug Safety 2003; 26(5): 293–301. (d) Bate
A, Lindquist M, Edwards IR, Olsson S, Orre R, Lansner A et al., A
Bayesian neural network method for adverse drug reaction signal
generation, Eur J Clin Pharmacol. 1998; 54(4): 315–321.
Disproportionality analyses:
There are various methods for conduction signal detec-
tion through the use of disproportionality analyses.
These methods to some degree employ the concept
of expected proportional reporting and calculate an
observed versus expected statistic for that particular
database. Using the example of calculating the PRR, the
measure is most accurate when using large databases
such as the large spontaneous reporting databases men-
tioned above.
This basic PRR is simple to calculate and uses a
2 × 2 table:
Drug of
Interest
All Other Drugs in
database
AE of Interest A B
All Other AEs in
database
C D
Note: PRR = A(A + C)/B(B + C).
For example:
_Drug X Drug of Interest All Other Drugs in database
AE of Interest 345 291
All Other AEs 6901 14556
Note: PRR = 345/6901 (345 + 6901)/291 (291 + 14556) = 1.67.
In words, the proportion of a particular AE divided
by all AEs seen with the drug of interest is divided by
the proportion of this AE divided by all AEs seen with
all drugs in the database. In the previous example, if
3.28% of all AEs seen with drug X are chest pain and
1.96% of all AEs seen with all the other drugs in the
database (excluding drug X and its AEs) are chest pain,
then the PRR is 3.278%/1.96% or 1.67. This means
that there are (dis)proportionately more chest pain
AEs with drug X compared with all the other drugs
in the database, and this is noteworthy as a possible
signal of disproportionality that should be evaluated
further.
When the PRR is calculated for all AEs in the data-
base, every PRR will either be below 1.0 or above 1.0. In
theory, disproportionality values above 1.0 would sug-
gest that an AE is more frequently reported for drug X
than for other drugs in the database. It is important to
remember that a signal of disproportionality does not
The Mathematics of Adverse Events 261
equate with risk. There are a number of reasons why
events may be presented as disproportionate. For exam-
ple, the event may already be on the product label, the
event may be related to the underlying disease being
treated, the event may more likely be related to a con-
comitant condition or medication, or there may be
duplicate reporting of the same case. Although there
are more than 80,000 terms in MedDRA, dispropor-
tionality analyses are usually conducted at the Preferred
Term (PT), Higher Level Term (HLT), the Higher Level
Group Term (HLGT) levels, or using SMQs (Stan-
dard MedDRA Queries) as well as MedDRA developed
adverse event term groupings. Typically, predetermined
thresholds are used to put guiderails around the utility
of a PRR estimate. A threshold often used with the PRR
is based on parameters where the PRR is 2 or higher,
the corresponding Chi-Square is 4 or higher, and the
minimum number of cases for the drug/event combina-
tion is 3 or higher. The higher the PRR, the greater the
specificity but the lower the sensitivity of the estimate.
Alternatively, one might simply take the 10 or 20 high-
est PRRs and evaluate those regardless of how high the
PRR is above 1.0.
There are other issues with using disproportionality
estimates. When the database is small or inappropriate,
the estimates may lack validity. Some examples of prob-
lems are listed below:
1. For serious and rare AEs adding or subtracting
one case can markedly alter the PRR results. For
example, if there is one myocardial infarct (MI)
in the four- patient database of drug X, this gives
an incidence of 1/4 or 25%. Taking away the one
case or adding one more MI would change the
rate to 0% (0/3) or 40% (2/5). If the database
had 1,000/4,000 cases, adding one or taking one
away would have a negligible effect.
2. If the database is incomplete, meaning is doesn’t
contain “all” of the product safety data, or cap-
tures criteria differently per protocol, it may
not be appropriate to compare the reporting
frequency of a particular AE in the population
treated with drug X against the frequency of that
AE in the whole AE database. If the treatment for
drug X is for, say, breast cancer, and is given only
to elderly women, then comparing the frequency
of an AE in the elderly female population versus
the whole database in which elderly women are
not predominant may give misleading results.
3. Another issue may occur if drug X is frequently
prescribed with drug Y and drug Y (“bystander”)
is known to produce a particular AE. Unless this
is accounted for, it may appear in a simple PRR
that drug X caused the AE when it was probably
due to drug Y.
Finally, if the safety database used for the denomi-
nator of the PRR is small or has a high proportion of a
particular type of patient or disease this may also pro-
duce flawed PRRs. If one has a sufficiently large data-
base, the PRR can be programmed to run periodically
(e.g., monthly or quarterly) using the appropriate filters
to generate possible signals. This method will be less
useful if MedDRA coding is not crisp and correct. As
always, the issue here is generating too many signals
with too many false positives for the personnel available
to review the signals.
Other Data Mining Methods
Although the disproportionality method is commonly
used by companies and health agencies to screen and
detect signals, other methods have been developed to
compensate for some of these above-mentioned prob-
lems. Some of the other methods are found in the broad
category of “Bayesian approaches”. These methods
account for the number of cases (cell sizes) and decrease
the sensitivity of the PRR score if the cell sizes are small.
One method, the Bayesian confidence propagation neu-
ral network (BCPNN), was developed by the Uppsala
Monitoring Centre and is used for signal detection in
their database. Other methods, such as the Gamma
Poisson Shrinker (GPS) and the Multi-item Gamma
Poisson Shrinker (MGPS), are also used in an attempt
to make the PRR more useful. These methods have been
used by various health agencies, including the Food
and Drug Administration (FDA), the European Medi-
cines Agency (EMA), and the Medicines and Healthcare
Products Regulatory Agency (MHRA) which is the Brit-
ish pharmaceuticao regulator. Treatment of these meth-
odologies in detail is beyond the scope of this book, and
the reader is referred to the standard textbooks of phar-
macoepidemiology. A good, approachable summary of
262 Cobert’s Manual of Drug Safety and Pharmacovigilance
the field is available in GVP Module IX Addendum I —
Methodological aspects of signal detection from spon-
taneous reports of suspected adverse reactions (EMA
Good PV Practices Web page).
10
A brief note on other terms you may run across:
Event rate — The number of subjects experiencing an
AE as a proportion of the number of people in the pop-
ulation at risk over a specific period of time. For exam-
ple, 5 per 1,000 person-days.
Absolute risk — The probability of occurrence of an
AE in patients exposed to a drug. For example, one
may say the absolute risk of a myocardial infarction
with drug X is 5%. Obviously, this value is often hard
or impossible to obtain and is one reason that pharma-
covigilance exists.
Absolute risk reduction — The arithmetic difference
between two absolute risk rates. For example, the abso-
lute risk of a myocardial infarction with drug X is 5% and
with drug Y 2%. The risk reduction is 5% − 2% = 3%.
Relative risk (or risk ratio) — In a clinical trial or in
an observational cohort study, the ratio between the rate
of an adverse outcome (e.g., an AE) in a group exposed
to a treatment and the rate in a control group. For
example, the rate of myocardial infarction in the drug
X group is 5% and in the control group 2.5%. The ratio
(relative risk) is 5%/2% = 2.5%.
Relative risk reduction — The difference in event
rates between two groups, expressed as a proportion of
the event rate in the untreated group.
Odds ratio — Also known as estimated (or approxi-
mate) relative risk. In case-control studies concerning
drug safety, the ratio between the rate of exposure to a
suspect drug in a group of cases (with the AE) and the
rate of exposure in a group of non-cases (i.e., controls
without the AE). Like relative risk from cohort studies,
the odds ratio is a useful estimation of the strength of
potential cause–effect relationships. This parameter is
often used in systematic reviews and meta-analyses.
10
See also “Practical Aspects of Signal Detection in Pharmacovigi-
lance,” Report of the CIOMS Working Group VIII. Counsel for the
International Organizations of Medical Sciences, Geneva, 2010.
See also http://www.ema.europa.eu/docs/en_GB/document_library/
Scientific_guideline/2017/10/WC500236405.pdf.
Risk difference or attributable risk — The difference
between the rate of an adverse outcome (e.g., an AE) in
a group exposed to an experimental drug and the rate
in a control group.
Number needed to harm (NNH) — Also called num-
ber needed to harm one. The NNH is the number of
patients that must be exposed to a drug to produce an
AE/ADR in one patient. Exposure may, for example,
be one course or 1 year of treatment. For example,
one could calculate based on a study that the number
needed to produce one case of rhabdomyolysis with a
particular statin is 3,500 patients.
Number needed to benefit (NNB) — A similar concept
to the NNH, but reflects the number needed to receive
a positive effect from the drug. For example, one might
need to treat three patients with a particular statin to get
a positive effect (e.g., cholesterol reduction).
Benefit–risk ratio — Various techniques have been
developed using such data as NNH and NNB to calculate
an actual number for the benefit–risk ratio. Although a
number of quantitative approaches have been investi-
gated, it is not possible to calculate an accurate “ratio”
using estimates; however, if one can calculate the NNH
and the NNB for the particular drug one can calculate
a benefit–risk ratio. This is rarely done, however, as the
data are incomplete and inaccurate.
Confidence intervals — Most studies are based on
samples, not entire populations, which adds an element
of uncertainty and unreliability to the results because
the entire population was not studied. Thus, we can-
not be certain that the 15% of the study population that
had serious AEs represents the true value for the whole
population rather than just for the smaller sample. The
confidence interval represents the range of the correct
or true value for the whole population and gives an idea
of the reliability of the data and of the estimate. One can
calculate various levels of “assurance”, 90%, 95%, 99%,
99.9%, and so on, for the confidence interval. Usually,
the 95% level is used. The narrower or smaller the dis-
tance between the upper and lower values of the confi-
dence interval (called the confidence limits), the more
accurate the estimate. The more patients in the study
usually produce a narrower (better) confidence interval.
CHAPTER
263
24
Pharmacoepidemiology:
Its Practical Use in the
World of Drug Safety
T
his chapter is not meant to be
an introduction to epidemiol-
ogy or pharmacoepidemiology.
There are many excellent textbooks
and references in those fields. Rather,
this chapter attempts, briefly, to place
pharmacoepidemiology in the context
of its use in the practical world of drug
safety.
Introduction
Epidemiology is the study of the distribution and
determinants of diseases in populations. Basically,
epidemiology is a discipline that provides tools for
assessing diseases and/or exposures within a defined
population. Epidemiology studies may provide sim-
ple, descriptive statistics such as disease incidence
and prevalence as determined in a natural history of
disease study or may use more analytic methods to
compare disease rates between two groups of indi-
viduals and can be hypothesis testing or hypoth-
esis generating in nature. Associations that have
been identified through epidemiologic techniques
include smoking and lung cancer, high blood pres-
sure and heart disease, increasing maternal age and
risk of trisomy 3 in the baby.
Pharmacoepidemiology is a subset of the broader
discipline of epidemiology. As defined by the Inter-
national Society of Pharmacoepidemiology (ISPE),
pharmacoepidemiology is the study of the utiliza-
tion and effects of drugs in defined populations.
Pharmacoepidemiology is an important tool in in
assessing drug safety when used in conjunction
with information from clinical trials and from the
spontaneous reporting system which is a database
of voluntary reports of adverse events in patients
receiving various drugs. Unlike the randomized
double-blind clinical trial which is the main study
design submitted to regulators for drug approval,
264 Cobert’s Manual of Drug Safety and Pharmacovigilance
pharmacoepidemiology plays a critical role in
understanding both risks and unintended bene-
fits of drugs, can identify subgroups of patients at
higher risk of an adverse event, and can provide key
supporting information about risks to patients in a
real world setting that may keep a drug on the mar-
ket despite a serious identified risk.
Pharmacoepidemiology as a discipline is popula-
tion-based rather than based on individuals or patients.
Broadly, it can be used to describe treatment patterns or
drug utilization within a specific disease or therapeu-
tic area. Other uses include determining the number of
new cases of a specific condition in a defined population
(such as geographic region, or special populations such
as neonates, children, elderly, during pregnancy). More
specifically, analytic epidemiologic methods can be used
to assess risk such as determining the risk of a specific
outcome in patients exposed to a specific drug compared
to background risk or compared to patients exposed to
a different therapeutic agent. Analytic methods can also
be used to determine factors that can contribute to risk
or comparative risk (risk factors) or can identify fac-
tors that are driving the risk rather than the exposure of
interest (confounding factors) which may superficially
ascribe an outcome to a particular exposure, when it was
actually related to the confounding factor. Sophisticated
modelling analytics can be used to examine the individ-
ual and shared contributions of individual risk factors or
interactions between risk factors.
Pharmacoepidemiology is an important tool in the
evaluation of drug safety. It complements the experi-
mental and interventional randomized clinical trial
which tests the safety and efficacy of drug products
between defined groups of participants. Well-run clin-
ical trials can provide greater rigor and confidence in
the study findings, but are generally too small to iden-
tify rare risks, risks with long latency periods, or small
increased risks added to a large background risk. Phar-
macoepidemiologic studies evaluate risks as the drug
products would be used in the real world rather than in
the tightly controlled confines of a clinical trial.
At the other end of the drug safety spectrum is the
spontaneous reporting system which consists of volun-
tarily reported adverse events that occur once a drug
is approved. Signals of rarely occurring adverse events
can be identified through spontaneous reports as well
as signals or events occurring after long latency periods
such as cancer and events that occur during pregnancy.
Because spontaneous reports do not have an underly-
ing defined population and may include co-morbidities
that were excluded from clinical trials, they cannot be
used to determine incidence or to describe comparative
risks quantitatively. Pharmacoepidemiology bridges
the gap between the signal-generating use of the spon-
taneous reporting system, and the controlled, experi-
mental hypothesis testing of the clinical trial. These
three methodologic tools together provide a more com-
plete understanding of drug-related risks than can be
provided by any of these tools alone. In the heavily
regulated arena of pharmaceutical safety, pharmacoep-
idemiology can be used to identify potential and real
risks relating to serious adverse events (SAEs) through
signal detection techniques. Other methods can then
be used to confirm and quantify the risk associated
with the signal or to rule it out. Such studies can rarely
answer questions about causality but rather give infor-
mation on probabilities and statistical associations.
Pharmacoepidemiology continues to grow in value
as a drug safety tool as automated tools are developed,
Real World Evidence (RWE) databases such as insur-
ance claims data and Electronic Medical Records (EHR)
become increasingly available, and regulatory expecta-
tions as to the manufacturer’s role and responsibility for
assessing the benefits and risks of their products con-
tinues to expand.
Tools for Evaluating Drug
Safety
The Randomized Controlled
Clinical Trial
To understand the role that pharmacoepidemiology and
observational methodologies play in evaluating drug
safety, it is important to understand the strengths and
limitations of the randomized clinical trial. The random-
ized controlled clinical trial (RCT) is the design method
Pharmacoepidemiology: Its Practical Use in the World of Drug Safety 265
that is required by global regulators to demonstrate the
efficacy and safety of a drug product to support prod-
uct approval. Participants in RCTs are selected by ran-
domization to receive one compound (experimental) to
another (comparative compound or routine practice for
example). The randomization component of RCTs con-
trols for factors other than treatment (including both
known and unknown bias) that may alter or explain
the drug effects so that the efficacy and safety findings
are most likely to result from differences between the
treatments themselves. RCTs have detailed protocols
which provide outline purpose and objectives, inclu-
sion and exclusion criteria, patient drop out, defini-
tions of outcomes, measurement tools, pre-identified
analytic methods, length of follow up, and identifica-
tion of treatment emergent adverse events among many
other factors. Because restrictions of who within a dis-
ease area are included in the RCT as well as restrictions
on other exposures or concomitant medications, the
RCT overall, does not and is not intended to reflect how
the drug would be used in general practice.
In addition to randomization, another important
component of the RCT is the degree to which the trial
is blinded. Blinding refers to the extent to which study
participants or investigators and their teams are igno-
rant as to which study compound the participants are
receiving. Blinding increases the rigor of the study by
eliminating certain factors other than the drugs being
tested that may alter or explain any observed differences
between treatment groups. The trial may be single-blind
(the subject does not know what treatment has been
received) or double-blind (neither the subject nor the
investigator knows what treatment has been received).
Randomized double-blind clinical trials are com-
plex and resource-intensive to implement and are
considered the gold standard of clinical research. RCT
studies are conducted primarily during phases II and
III of drug development and the results are sufficiently
powered to describe the efficacy of the drug. Although
safety is also an important outcome examined during
the clinical trial, the relatively low number of partic-
ipants studied and the short period of follow-up only
allows for the identification of more commonly occur-
ring adverse events that occur within a short period of
time after drug administration.
Pharmacoepidemiologic
Study Designs
As previously mentioned, pharmacoepidemiology stud-
ies are observational by design. There are three major
types of pharmacoepidemiology studies: cohort stud-
ies, case-control studies, and cross-sectional studies.
The first two study types are most commonly used in
pharmacoepidemiology and will be discussed in greater
detail. Each study type has distinct strengths and limita-
tions which factor in as to what inferences can be drawn
from each study design.
The Cohort Study
The cohort study is the strongest of the three epide-
miologic study designs. A cohort study with a single
cohort, or a group that is comprised of a common
factor such as having a particular disease, is typically
used to conduct natural history of disease studies. An
example would be following a group of individuals
with schizophrenia in anticipation of a new drug for
schizophrenia being approved. By understanding the
characteristics and disease components of the target
population, the pharmacoepidemiologist will see what
types of conditions occur in that population that occur
outside of exposure to the new drug. In single cohort
studies, there is no comparison group and follow up is
observational in nature. The occurrence rate or back-
ground incidence rate can be calculated such that the
background rate for sudden death, for suicidality, or
for liver failure can be determined. This is useful infor-
mation to have early (before approval) to compare to
information on adverse events that start being reported
once a drug is approved in order to put those events in
context.
A comparative cohort study is a study where two groups
of individuals with a different study factor, such as one
group with a drug exposure of interest and the other
group with no exposure, are followed prospectively
over time to identify outcomes of interest such as the
occurrence of adverse events. The comparative cohort
study can be conducted in real time, by identifying the
cohorts now and following them into the future, or the
266 Cobert’s Manual of Drug Safety and Pharmacovigilance
study can be a historical cohort study where the cohorts
were identified and followed over time in the past. With
both designs, incidence rates can be calculated as well
as comparing the incidence rate from one group to the
other group through the relative risk.
The following tables show an example of calculat-
ing relative risk.
Calculating Relative Risk and
Absolute Risk
Outcome
yes no Total
Took the Drug
Cohort 1: yes a b a+b
Cohort 2: no c d c+d
Relative risk = [a/(a + b)] ÷ [c/(c + d)] or the rate in exposed
divided by the rate in the unexposed.
Absolute risk = [a/(a + b)] – [c/(c + d)] or rate in exposed minus
rate in unexposed.
Example of Calculating Relative Risk
and Absolute Risk
Experienced bleeding
yes no Total Rate
Took
aspirin
Cohort 1:
yes
500 10,000 10500 0.048
Cohort 2:
no
150 10,000 10150 0.015
Relative risk = [500/(10500)] ÷ [150/(10150)] or 0.024÷0.015=3.2
or those who took aspirin had 3.2 times the risk of bleed
compared to those not taking aspirin
Absolute risk = [500/(10500)] – [150/(10150)] or rate in exposed
minus rate in unexposed. 0.048–0.015 = –0.033
In this example, we have 20,650 study subjects that
were divided between 2 cohorts. Cohort 1 was com-
prised of 10.500 subjects who took aspirin regularly
and Cohort 2 with 10,150 subjects who did not take
aspirin. The 2 cohorts were followed over time to see
who developed bleeding. At the end of the follow up
period, 500 subjects in Cohort 1 developed bleeding
while 150 in Cohort 2 experienced bleeding. As seen
in Table 2, the relative risk was 3.2 in the aspirin group
compared to the non-aspirin group indicating that
those taking aspirin were 3.2 times more likely to expe-
rience bleeding than those not taking aspirin. A rela-
tive risk of 1 indicates that the likelihood of getting the
adverse event is the same in the treatment group and
in the non-treatment group, while a relative risk find-
ing less than 1 would indicate that the treatment group
had LESS of a chance of experiencing the outcome of
interest than the non-treatment group. The absolute
risk shows the absolute risk of bleeding with aspirin use
controlling for the underlying risk of bleeding in the
unexposed population.
Strengths of the cohort design include the abil-
ity to calculate incidence rates and comparative inci-
dence rates (relative risk) which is very important in
drug safety, particularly when comparing incidence of
an adverse outcome in subjects taking one drug com-
pared to those taking another drug. Another strength is
the lack of recall bias which is bias which results when
subjects at one point in time to recall events or expo-
sures that occurred in the past and there is differential
recall between groups. This will be discussed more fully
in the case-control design. Limitations of the cohort
design are that they can be costly, labor-intensive, and
lengthy to conduct (over several years) if set up as a de
novo study where, for example, the study is set up at
drug approval and the study subjects are followed going
forward in time. This limitation can be reduced if using
historical data where the cohorts have been identified
at some point in time and then medical records which
have already been collected are examined going forward
from that point in time. Cohort studies can also require
large numbers of study subjects if the outcome of inter-
est is rare.
The Case-Control Study
While conceptually cohort studies are prospective in
that the design is based on going forward in collecting
information from the point of drug exposure, with case-
control studies, the design is retrospective in that both
drug exposure and outcome of interest have already
occurred. Typically, two groups of subjects, with or
without an outcome of interest, are identified, and either
the subjects are interviewed and/or medical records are
Pharmacoepidemiology: Its Practical Use in the World of Drug Safety 267
examined. to look for what proportion of subjects in
each outcome group had the exposure of interest. Put
another way, case-control studies determine the chance
(“odds” or “probability”) of having taken the suspect
drug in a group of subjects already suffering from an
adverse event and compares that with the odds of hav-
ing taken the drug in a group of subjects who did not
have the event. Large, automated databases such as
administrative medical claims data or electronic med-
ical record databases are sometimes used for this study
design.
When setting up a case-control study, it is advan-
tageous to select groups of cases (with the event) and
non-cases (without the event) that are relatively compa-
rable by matching the two groups by various potential
confounding factors such as age, gender, race, concom-
itant conditions among other factors so that the groups
are identical in all other aspects other than the expo-
sure to the drug of interest. Both groups should have
the same opportunity to receive the drug.
For example, if a hospital database is used as the
basis of the case-control study, the drug of interest
should have been on the pharmacy formulary for the
entire time period examined in the study. The sub-
jects’ medical histories are then examined to see which
subjects in each group used the drug in question. The
simplified data are then organized into a 2 × 2 table as
depicted in Table 3.
Example of Calculating the Odds Ratio
Myocardial
Infarction yes
Myocardial
Infarction no
Total
Cox-2
inhibitor yes
10 5 unknown
Cox-2
inhibitor no
50,000 50,000 unknown
Odds Ratio = (0.0002) ÷ (0.0001) = 2 or the those with
myocardial infarction were twice as likely to have taken a cox-2
inhibitor that those without myocardial infarction
An odds ratio value of greater than 1.0 is suggestive
of an association between the drug and the outcome of
interest. Generally, an odds ratio value of greater than
2.0 is believed to be fairly strong and supports an asso-
ciation. In our example, the odds ratio value of 2 is
suggestive of an association and further studies may be
needed to confirm or reject an association.
Advantages of case-control studies are that the
design is useful for studying very rare adverse events
because the investigator seeks out a data source where
this outcome is found in a sufficient number of sub-
jects. For rare outcomes, the odds ratio can be consid-
ered approximate to the relative risk or risk ratio of a
conceptual cohort study. Higher order statistical tech-
niques can be applied such as logistic regression for
the adjustment of covariates and risk factors that may
confound the association, e.g., severity of the disease
treated, or concomitant medications, and possible inter-
actions between drugs.
Case-control studies are fast and relatively inexpen-
sive; however, they are potentially liable to significant
bias both in the selection of the subjects for the two
groups and in the amount and quality of the drug expo-
sure and medical history data.
The Nested Case-Control Study
Nested case-control studies are studies of case-control
design that are nested (or conducted within) a cohort
study or a clinical trial. In both situations, the nested
case-control study is conducted within one cohort or
one treatment arm of a trial. This study design is useful
to examine whether there are predictors for a certain
outcome in a group of subjects with a similar exposure.
One example where a nested case-control study can be
used before approval is when a clinical trial identifies
a particular risk in the experimental treatment group.
It would be useful to understand if this risk applies
to all study subjects in that group, or if there are fac-
tors that could predict who may be a greater risk of
that finding. For example, a particular trial finding is
that of excessive weight gain in the experimental drug
group. A nested case-control study is then conducted
in that study group to see if there are other factors that
could predict who may be at greater risk of excessive
weight gain. These factors can include age, gender,
race, family history, concomitant conditions or medi-
cations among other factors. If a subgroup of patients
is identified with greater risk such as increased age,
268 Cobert’s Manual of Drug Safety and Pharmacovigilance
that finding could be added into the product informa-
tion as a precaution.
Confidence Intervals
An important part of any experimental or observation
study is whether a finding meets statistical significance.
Many studies rely on the p-value to determine statisti-
cal significance, but another tool that brings additional
information to the significance finding is the use of
confidence intervals. The confidence interval reflects
the uncertainty of the study finding in that particular
sample of study subjects.
If a particular study finds that 15% of the users of
drug A had a serious adverse event, the confidence inter-
vals can help determine the certainty that this finding
is correct. The confidence interval describes the upper
bound and lower bound of where the “true” value is
with a pre-determined degree of certainty. Typically,
95% confidence intervals are used. An example is that
a study finds that a measure of risk is 3.3 and the 95%
confidence intervals are 2.4 and 4.1. A number of con-
clusions can be drawn from examining the confidence
intervals. First, the finding that the lower bound of the
confidence interval is greater than 1 indicates that the
finding is statistically significant. The values of the con-
fidence intervals demonstrate that there is 95% certainty
that the real value of the association is between 2.4 and
4.1 and the finding that the lower bound is greater than
2 indicates that the finding may be substantive. Wider
confidence intervals indicate greater uncertainty in the
finding which is typically seen with small numbers of
subjects. If the confidence intervals include the value
of 1, the finding has not reached statistical significance
but the distance of the value from 1 can suggest that the
finding either does or does not approach significance. If
both values of the confidence intervals are below one,
the finding is statistically significant and in the opposite
direction of the null hypothesis.
Conclusions
With the arrival of formal risk management as an inte-
gral part of the development and life span of all drugs,
the fields of pharmacoepidemiology and drug safety are
now more tightly linked than ever.
Health authorities require epidemiologic safety
studies, particularly as post-marketing commitments
or requirements. Large databases and practitioners who
know how to conduct these studies have become more
and more available and the methodology has become
more refined and automated. Many pharmaceutical
companies now have risk management/pharmacoepide-
miology departments to handle these studies.
Indeed, the role of pharmacoepidemiology in risk
assessment and signal detection has become more
prominent as risk management programs become
increasingly active over the entire life cycle of products.
Frequently Asked Questions
Q: I never was particularly gifted with math
and numbers. Do I really have to learn this
stuff? Do I really need it in drug safety?
A: Yes, you really need it to interpret and make
informed decisions. As electronic medical records
become more widespread and large databases are used
for epidemiology and observational safety studies,
data mining, Bayesian analysis, and other statistical
techniques, numerical literacy — as it is called — will
be very useful and probably obligatory at some point.
Further, as regulatory agencies have embraced epide-
miological principles and methods, other stakeholders
will increasingly need to understand principles and
practices of pharmacoepidemiologv as applied to drug
safety.
Q: Do regulators accept the findings of phar-
macoepidemiology studies?
A: Not only do regulators accept the findings of
well-conducted pharmacoepidemiology, these studies
are often mandated by regulators to be conducted after
the drug is marketed as a condition of approval as part
of the post approval commitments.
Q: Are the findings of pharmacoepidemiology
studies more decisive than clinical trials in
assigning causality?
A: Clinical trials have the more scientifically rigorous
study design for formally testing certain aspects of cau-
sality; however, pharmacoepidemiology studies may
complement and support clinical trial findings and can
Pharmacoepidemiology: Its Practical Use in the World of Drug Safety 269
also suggest potential areas of risk that clinical trials are
too small or too short in duration to identify.
Q: This seems like a much bigger topic. Are
there experts on this topic out there?
A: Yes... a few pages on this subject won’t make you an
expert of course! There are advanced graduate degrees
in pharmacoepidemiology and many experts in this
field. It’s important to have someone with this skill set
within an organization or available as a consultant or
contractor.