Although evidence-based medicine has grown over the past 2 decades and has become fundamental to oncologic practice, major questions remain. In an ideal world, clinical decisions would be based on data from exquisitely designed randomized controlled trials (RCTs). But RCTs are expensive, impractical, and can be unethical if they question entrenched practices that have become the standard of care. In addition, they are designed to answer questions of efficacy: the effect of an intervention compared with a placebo when all other variables are controlled. They poorly mimic oncologic practice in the real world, and as a result, investigators are turning to studies of effectiveness, which examine the effects of an intervention under real-world conditions.
In principle, comparative effectiveness research (CER) is not a new idea; the Institute of Medicine broadly defines it as the synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care. New research tools and techniques, however, have made it possible to pursue CER in a more effective manner. One of the formidable resources that oncologic researchers now have to explore CER questions is large database analysis. With large amounts of patient data, databases allow researchers to conduct powerful retrospective studies and can offer a robust initial approach to certain questions of effectiveness. Researchers, however, must not only consider the strengths and weakness of a large database study in theory but also the various limitations of specific databases if they are to undertake a serious project in oncologic CER.
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