Considerations for Observational Research Using Large Data Sets in Radiation Oncology
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan (United States)
- Departments of Radiation Oncology and Medical Ethics and Health Policy, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania (United States)
- Department of Radiation Oncology, Harvard Medical School, Boston, Massachusetts (United States)
- Department of Radiation Oncology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina (United States)
- Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
- Department of Medicine, Section of Hospital Medicine, The University of Chicago, Chicago, Illinois (United States)
- Department of Radiation Oncology, Division of Radiation Oncology, and Department of Health Services Research, The University of Texas MD Anderson Cancer Center, Houston, Texas (United States)
- Yale School of Medicine, New Haven, Connecticut (United States)
The radiation oncology community has witnessed growing interest in observational research conducted using large-scale data sources such as registries and claims-based data sets. With the growing emphasis on observational analyses in health care, the radiation oncology community must possess a sophisticated understanding of the methodological considerations of such studies in order to evaluate evidence appropriately to guide practice and policy. Because observational research has unique features that distinguish it from clinical trials and other forms of traditional radiation oncology research, the International Journal of Radiation Oncology, Biology, Physics assembled a panel of experts in health services research to provide a concise and well-referenced review, intended to be informative for the lay reader, as well as for scholars who wish to embark on such research without prior experience. This review begins by discussing the types of research questions relevant to radiation oncology that large-scale databases may help illuminate. It then describes major potential data sources for such endeavors, including information regarding access and insights regarding the strengths and limitations of each. Finally, it provides guidance regarding the analytical challenges that observational studies must confront, along with discussion of the techniques that have been developed to help minimize the impact of certain common analytical issues in observational analysis. Features characterizing a well-designed observational study include clearly defined research questions, careful selection of an appropriate data source, consultation with investigators with relevant methodological expertise, inclusion of sensitivity analyses, caution not to overinterpret small but significant differences, and recognition of limitations when trying to evaluate causality. This review concludes that carefully designed and executed studies using observational data that possess these qualities hold substantial promise for advancing our understanding of many unanswered questions of importance to the field of radiation oncology.
- OSTI ID:
- 22420404
- Journal Information:
- International Journal of Radiation Oncology, Biology and Physics, Vol. 90, Issue 1; Other Information: Copyright (c) 2014 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); ISSN 0360-3016
- Country of Publication:
- United States
- Language:
- English
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