Hypothesis-Testing Methods

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Chapter: Pharmacovigilance: The Basis of Pharmacovigilance

Studies of this type compare cases with a disease with controls susceptible to the disease but free of it.


Case–Control and Case–Crossover Studies

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.

Cohort Studies

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.

Randomized Controlled Trials

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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