Studies of this type compare cases with a disease with controls susceptible to the disease but free of it.
HYPOTHESIS-TESTING METHODS
Studies
of this type compare cases with a disease with controls susceptible to the
disease but free of it. Using this method, the research compares the exposure
rate in the cases with the exposure rate in the controls, adjusting
statistically for factors that may confound the association. As with any formal
epidemiological or clinical study, great care has to be taken in the design.
Special attention is needed in case definition so that the cases truly
represent the specific outcome of interest (e.g. Stevens–Johnson syndrome and
not all cases of rash). It is also important to select an appro-priate control
group that represents the population that gave rise to the cases. Careful
design can mini-mize the amount of bias in a study; adequate control in the
analysis is also important. Case–control stud-ies have provided a substantial
body of evidence for major drug safety questions. Two notable examples are
studies that demonstrated the association between aspirin and Reye’s syndrome
(Hurwitz et al., 1987) and the
evaluation of diethylstilbestrol (DES) and vaginal cancer in the offspring of
mothers who took DES in pregnancy (Herbst et
al., 1974, 1975). More-over, a case–control study established the
protective effects of prenatal vitamin supplementation on the development of
neural tube defects (Werler, Shapiro and Mitchell, 1993). The final results of
these studies present a measure of the risk of the outcome associ-ated with the
exposure under study – expressed as the odds ratio. Only in very special
circumstances can the absolute risk be determined. Clearly, a fairly small
increase in the risk of a common, serious condition (such as breast cancer) may
be of far greater public health importance than a relatively large increase in
a small risk (such as primary hepatic carcinoma).
Case–control
studies are more efficient than cohort studies, because intensive data need
only be collected on the cases and controls of interest. Case– control studies
can often be nested within exist-ing cohort or large clinical trial studies. A
nested case–control study affords the ability to quantify abso-lute risk while
taking advantage of the inherent effi-ciency of the case–control design.
The
case–crossover design is a design very useful for the evaluation of events with
onset shortly after treatment initiation. In this design, cases, but not
controls, are identified. A drug association is evalu-ated through comparing
frequency of exposure at the time of the event with frequency of exposure at a
different time for the same individuals. This design is less subject to bias
than case–control studies because individuals serve as their own controls. As
with case– control studies, unless the experience is nested within a larger
cohort, it is not possible to estimate the abso-lute rate of events. For
special circumstances, the case–crossover design is a very powerful design in
pharmacoepidemiology.
These
studies involve a large body of patients followed up for long enough to detect
the outcome of interest. Cohort studies generally include an exposed and
unexposed group, but there are also single-exposure, disease or general
population follow-up studies and registries. Studies must be designed to
minimize potential biases. An advantage of the cohort study is its ability to
quantify both an absolute risk and a relative risk. Cohort studies can be
conducted prospectively, but such studies are usually expensive and
time-consuming. Retrospective cohort studies can be conducted within large
existing databases, provid-ing the advantage of the cohort study design and the
efficiencies inherent in studies using existing records.
Case–control
studies are particularly useful to confirm a safety signal relating to a rare
event (less than 1/1000). Cohort studies are useful when the outcome has not
already been identified or when multiple outcomes are of interest. Both
case–control and cohort studies can be conducted within large exist-ing
databases, assuming the required information is available.
An
example of current methodologies can be found in the Medicines Evaluation and
Monitoring Orga-nization (MEMO). MEMO achieves ‘record-linkage’ by joining
together general practitioner prescription data (the exposure data) with
hospital discharge summaries (the outcome data). This activity takes place in
Tayside, Scotland, where (uniquely in the United Kingdom) all patients have a
personal Commu-nity Health Number (CHNo), which is widely used by NHS
facilities of all types. Advantages include completeness, freedom from study-introduced
bias in data collection and timely availability of data for analysis. MEMO is
an example of the types of databases that have been established since the
mid-1970s that utilize data collected for other purposes. These databases have
been used to detect and quanti-tatively evaluate hypotheses regarding safety
signals.
Data
resources now exist in many countries, espe-cially in North America and western
Europe. Some examples of these data resources and application of these
databases to answer important safety questions will be described in further
chapters.
In
this method of study, a group of patients is divided into two in strictly
random order; one group is then exposed and the other not exposed, so that the
outcomes can be compared. The method is of great importance because random
assignment of treatment removes some of the biases possible in observational
studies. It is, however, of only limited (but important) use as a
pharmacoepidemiological tool because most serious ADRs are relatively uncommon;
randomized controlled trials (RCTs) used in such contexts can, therefore,
become unmanageably large and expen-sive. Large simple trials have become more
common over the last decade in evaluating safety and efficacy in special
circumstances, such as vaccine develop-ment, hormone replacement therapy and
treatments for common cardiovascular conditions.
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