Comparative effectiveness research (CER) can efficiently and rapidly generate new scientific evidence and address knowledge gaps, reduce clinical uncertainty, and guide health care choices. Much of the potential in CER is driven by the application of novel methods to analyze existing data. Despite its potential, several challenges must be identified and overcome so that CER may be improved, accelerated, and expeditiously implemented into the broad spectrum of cancer care and clinical practice. To identify and characterize the challenges to cancer CER, the authors reviewed the literature and conducted semistructured interviews with 41 cancer CER researchers at the Agency for Healthcare Research and Quality's Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) Cancer CER Consortium. Several data sets for cancer CER were identified and differentiated into an ontology of 8 categories and were characterized in terms of strengths, weaknesses, and utility. Several themes emerged during the development of this ontology and discussions with CER researchers. Dominant among them was accelerating cancer CER and promoting the acceptance of findings, which will necessitate transcending disciplinary silos to incorporate diverse perspectives and expertise. Multidisciplinary collaboration is required, including those with expertise in nonexperimental data, statistics, outcomes research, clinical trials, epidemiology, generalist and specialty medicine, survivorship, informatics, data, and methods, among others. Recommendations highlight the systematic, collaborative identification of critical measures; application of more rigorous study design and sampling methods; policy-level resolution of issues in data ownership, governance, access, and cost; and development and application of consistent standards for data security, privacy, and confidentiality. Cancer 2012.
- comparative effectiveness research
- data standardization
- Developing Evidence to Inform Decisions About Effectiveness (DEcIDE)
- research methods
- retrospective data