Recent legislation incorporated comparative-effectiveness research (CER) as a scientific mechanism to help improve health care.1 The law expresses particular interest in discovering which treatments work “in a real world setting” and encourages conduct of observational studies using data mining techniques of standardized electronic records.1,2 Ideally, CER will identify effective interventions in the subgroup of patients, since traditional randomized trials typically provide efficacy data for an “average” patient only.1,2 It is likely that the amount of observational research will increase significantly, especially studies involving data mining of large administrative databases and electronic medical records. However, epistemological arguments suggest that data mining efforts cannot provide definitive answers to the questions asked by the CER program. Rather, CER should be considered hypothesis-generating research aiming to inform future prospective studies that will invariably require new (and better) data collection.
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