Observational studies of drugs and mortality.

Randomized controlled trials of therapies for the primary and secondary prevention of cardiovascular disease increasingly are powered to study overall mortality. The interventions frequently treat asymptomatic risk factors among high-risk patients and have a diverse spectrum of potential adverse effects. Thus, medications such as clofibrate or encainide that successfully alter surrogate end points but increase the rate of death are of little clinical interest. Many believe that a new preventive therapy for cardiovascular disease should be used sparingly, if at all, until clinical trials establish equivalence or even superiority to existing treatments in terms of mortality from all causes. When randomized, controlled trials, for a variety of reasons, fail to provide data on overall mortality, should epidemiologic studies step into the breach? An observational post-marketing study by Wang et al. in this issue of the Journal (pages 2335–2341) compares conventional and atypical antipsychotic medications and shows that the former are associated with a 37 percent increased risk of death. Such data on mortality from all causes will be of keen interest to patients and their physicians. However, there are formidable challenges involved in conducting studies of mortality that are clinically relevant and scientifically accurate. A finding of an increased rate of death from any cause is always important. However, those who design and fund studies should consider whether negative results would be of similar scientific interest. That is, is death from any cause plausibly a sensitive indicator of the risks and benefits associated with a given medication? Mortality is always pertinent with regard to medications (such as lipid-lowering agents) used to treat asymptomatic patients, because the only benefit is prevention of future disease. For medications that have a broad spectrum of potentially serious side effects or that are used to treat patients at high risk of disease, overall mortality is an excellent integrated measure of overall benefit in comparison with risk. However, an exclusive focus on mortality may obscure important effects of medication,1 particularly when study findings are negative. For example, in the treatment of gastroesophageal reflux disease with proton-pump inhibitors, it probably would make little sense to base decisions about therapy and policy on negative results with regard to overall mortality in studies comparing these inhibitors with antacids. Among typical patients, the baseline mortality risk would be too low to provide adequate power, and even in high-risk populations, the further loss of power and potential bias introduced by deaths unaffected by these drugs would conceal medication effects. Furthermore, an analysis of overall mortality may contribute little to the elucidation of a biologic basis for a given drug effect. Recognizing that overall mortality often does not tell the full story, the investigators involved in individual clinical trials almost always report other primary end points. In a randomized, controlled trial, study treatments are initiated at or after the beginning of follow-up. This approach has two important advantages. First, it ensures the identification of adverse events that occur early in therapy, which is often a period of increased risk.2 For example, in comparisons of medical and surgical therapies, follow-up is begun before surgery to account for perioperative events. Second, prognostic factors can be measured before they are influenced by the treatments. Observational studies can emulate this characteristic of randomized, controlled trials with the inclusion of new user designs,2 which synchronize the beginning of follow-up with the start of drug use. Like randomized, controlled trials that study mortality, observational studies usually require very large sample sizes. Thus, most post-marketing studies use data that are already available, such as those that are obtained from automated administrative databases or those from multipurpose studies. Among the minimum requirements for the use of such databases are a defined population for which entries and exits are tracked, information about the occurrence and date of death (and, ideally, the cause), data regarding potential prognostic factors (i.e., confounders) that may differ among the study groups, and ongoing quality control and validation of crucial elements of the data. The database must include information that permits the tracking of drug use on a day-by-day basis. The capacity to define the