Although premarketing clinical trials are required for all new drugs before they are approved for marketing, with the use of any medication comes the possibility of adverse drug reactions (ADRs) that may not be detected in the highly selected populations recruited into randomized clinical trials. A primary aim in pharmacovigilance is the timely detection of either new ADRs or a relevant change in the frequency of ADRs that are already known to be associated with a certain drug that may only be detected in more typical clinical populations because of their greater range of illness severity and more comorbid illness and use of other medications (14
). Moreover, less common ADRs will require larger populations to be detected. Historically, pharmacovigilance relied on case studies such as the Yellow Card system in Britain and case control studies (41
). The Uppsala monitoring center reports (http://www.who-umc.org
) and classic Venning publications also highlighted the importance of individual case reports for signal detection (59
More recently, several large-scale postmarketing studies have been designed to detect ADRs. However, these studies are often unrepresentative of the potential users of a drug, have incomplete data, have short follow-up, and have inadequate sample size for rare ADRs. Furthermore, a control group (i.e., patients suffering from same disease but undergoing no active treatment) is often unattainable except in very special circumstances (14
In the following sections, we review a variety of approaches () for studying ADRs ranging from spontaneous reports to ecological studies to analyses of medical claims databases. Our review focuses on both design and analytic issues, highlights strengths and limitations, and is illustrated with examples of the relationship between antidepressant use and increased suicidality. We begin our discussion with perhaps the weakest data: spontaneous reports of AEs, which are subject to numerous sources of bias (under-reporting, media attention effects, poor quality of the data in terms of large amounts of missing data on relevant demographic characteristics, and duplication of reports). Next, we move to ecological data, which have the benefit of often covering an entire population of interest and therefore permit analysis of extremely rare events (e.g., child suicide) but are limited by the fact that we do not know if the same individual who experienced an AE is the same individual who took a particular medication. Next, we consider meta-analysis of randomized controlled trials (RCTs). This is a favorite approach of the psychopharmacological division of the U.S. Food and Drug Administration (FDA) and involves pooling information from multiple RCTs, typically placebo controlled. The obvious advantage of this approach is that it enjoys the scientific and statistical benefits of randomization; however, there are numerous limitations, including entrance criteria that may exclude those patients at highest risk of the AE (e.g., suicidality), small sample sizes consisting of patients monitored for short time periods, and ascertainment biases that are associated with a focus on spontaneously reported AEs. Finally, we review various approaches to the design and analysis of studies that are based on large-scale medical claims databases. Medical claims data are often based on large enough samples to evaluate all but the rarest AEs. Furthermore, they are more generalizable to routine practice than RCTs are because they do not have exclusion criteria beyond that which gets one into the specific health care system in the first place. The limitation is that these studies are not randomized; therefore, results can be biased owing to factors such as confounding by indication, in which patient characteristics lead to treatment of a particular type and it becomes difficult to disentangle the effects of treatment from the characteristics of the patients that lead to treatment.
Summary of available methodologiesa