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Adverse Events: The More You Search, the More You Find
- John P.A. Ioannidis, MD;
- Cynthia D. Mulrow, MD, MSc, Deputy Editor; and
- Steven N. Goodman, MD, PhD
- From the University of Ioannina School of Medicine, Ioannina 45110, Greece; American College of Physicians, Philadelphia, PA 19106; and Johns Hopkins School of Medicine, Baltimore, MD 21205.
People want reliable information about potential harms of medications. Concerns about possible adverse effects guide therapy selections, and unpleasant surprises about unsuspected harms cause anxiety and make headlines (1). We often rely on compendia and product inserts for information about such effects. These materials offer litanies of possible adverse events, sometimes accompanied by an estimate of how often those events might occur. From whence are these estimates derived? What do they really mean? How can we better measure and understand how many and what kinds of harms may be caused by medications?
Sources of Evidence about Medication-Related Harms
We can identify medication-related harms from any of a variety of sources of evidence, including case reports, observational studies, and randomized trials (2). The various sources (for example, observational studies vs. randomized trials) may provide different estimates and inferences about the harms (3). The optimal source for assessing the harm depends on many factors. These include whether the intent is to establish causality; the underlying frequency and severity of the event; previous expectations and knowledge about the potential event; how easily the event can be measured; how soon the event is likely to occur after the start of therapy; and whether the event is reversible.
Compendia and product inserts usually list severe or unusual adverse events that are allegedly associated with a particular drug. The authors of these materials often derive this information from sources such as case reports, postmarketing surveillance data, or observational studies (4). The frequency and causality of the events are often unclear because the source may not have evaluated a sufficient sample of people at risk for an adverse event, or appropriate comparison groups may have been lacking. Knowing whether an adverse event occurs in 0.1%, 1%, or 10% of patients requires reliable numerators (numbers of patients with adverse events) …
