Curriculum in CardiologyLeveraging observational registries to inform comparative effectiveness research
Section snippets
Evolution of observational CER efforts
Current observational databases, most of which are now electronic, were borne out of business process need (eg, claims databases); however, provider-led databases focusing specific diseases and/or patients have also emerged, with the earliest examples being the Duke Databank for Cardiovascular Disease established in 196911 and later the Coronary Artery Surgery Study started in 1975 to assist the National Heart, Blood, and Lung Institute to develop a program to support research investigations.12
Beyond RCTs—opportunity for observational CER
The RCT is the “criterion standard” methodology providing the highest level of evidence to inform efficacy of a particular intervention or therapy.1 Through randomization and, in most cases, treatment blinding, RCTs control for unmeasured selection and treatment biases, as well as potentially other biases, to determine the relative risk and benefits of a particular treatment. Evidence from these studies can be used for regulatory approval, to support clinical guidelines, and to inform policy
Observational data sources
Database sources for observational studies that currently exist are heterogeneous in terms of the type of patient population followed, data elements collected, geography, and funding mechanism. Table I demonstrates some of the potential sources of a variety of observation databases. Claims databases have the advantage of being large and capturing almost every interaction with the health care delivery system. However, because the data are used for claims and billing purposes, they lack detailed
Role of observational data to inform CER
To fill the evidence void, the growing availability of electronic databases allows the ability to answer questions that cannot be answered by traditional RCTs. These observational data have the potential to rapidly and effectively fill many of the gaps in therapeutic decision making. It is hoped that the insights gained from CER using these data will be leveraged to improve our understanding of therapeutic options as well as to reduce health care expenditures by identifying efficacious and
Limitations of observational CER
Comparative effectiveness research analyses are subject to 4 key biases that limit the use of observational data sources: selection, detection, performance, and attrition.39 (1) Selection bias refers to differences that may exist among comparator groups that arise from physician treatment selection, patient treatment choices, or treatment assignments due to patient characteristics, such as gender, age, income, education, race, etc. (2) Detection bias occurs when a difference occurs in the
Evidence generation and dissemination
Comparative effectiveness research is a key component in the cycle of therapeutic development and evidence generation (Figure 1).43 In this cycle, key to the success of observational CER investigations is asking the right questions. As posited by the Agency for Healthcare Research and Quality6 and the Federal Coordinating Council for Comparative Effectiveness Research,44 CER investigations should address important and specific questions where there is uncertainty for clinical decision making by
Conclusions
The ARRA 2009 has accelerated CER efforts and provided the resources to further develop and build new clinical electronic repositories for observational research. Comparative effectiveness research offers the opportunity to provide insights into the outcomes and costs of existing and even new therapeutics by leveraging observational databases to inform decision making. The recognition that evidence for many therapies used in routine clinical practice and associated outcomes is lacking endorses
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- Institute of Medicine, Roundtable on Evidence-based Mediciner. July 27, 2006. Available at:...