Smith--High Value Secondary Data Analysis article

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Conducting High-Value Secondary Dataset Analysis: An Introductory Guide and Resources Alexander K. Smith, MD, MPH 1,2 , John Z. Ayanian, MD, MPP 3,4,5 , Kenneth E. Covinsky, MD, MPH 1,2 , Bruce E. Landon, MD, MBA, MSc 3,4,6 , Ellen P. McCarthy, PhD, MPH 3,6 , Christina C. Wee, MD, MPH 3,6 , and Michael A. Steinman, MD 1,2 1 Division of Geriatrics, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA; 2 Veterans Affairs Medical Center, San Francisco, CA, USA; 3 Harvard Medical School, Boston, MA, USA; 4 Department of Health Care Policy, Harvard School of Public Health, Boston, MA, USA; 5 Division of General Medicine, Brigham and Women s Hospital, Boston, MA, USA; 6 Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA. Secondary analyses of large datasets provide a mecha- nism for researchers to address high impact questions that would otherwise be prohibitively expensive and time-consuming to study. This paper presents a guide to assist investigators interested in conducting second- ary data analysis, including advice on the process of successful secondary data analysis as well as a brief summary of high-value datasets and online resources for researchers, including the SGIM dataset compendium ( www.sgim.org/go/datasets ). The same basic research principles that apply to primary data analysis apply to secondary data analysis, including the development of a clear and clinically relevant research question, study sample, appropriate measures, and a thoughtful analytic approach. A real-world case description illustrates key steps: (1) define your research topic and question; (2) select a dataset; (3) get to know your dataset; and (4) structure your analysis and presentation of findings in a way that is clinically meaningful. Secondary dataset analysis is a well-established methodology. Secondary analysis is particularly valuable for junior investigators, who have limited time and resources to demonstrate expertise and productivity. KEY WORDS: large datasets; secondary analysis; publicly available; guide; resources. J Gen Intern Med 26(8):920 9 DOI: 10.1007/s11606-010-1621-5 © The Author(s) 2011. This article is published with open access at Springerlink.com INTRODUCTION Secondary data analysis is analysis of data that was collected by someone else for another primary purpose. Increasingly, generalist researchers start their careers conducting analyses of existing datasets, and some continue to make this the focus of their career. Using secondary data enables one to conduct studies of high-impact research questions with dramatically less time and resources than required for most studies involving primary data collection. For fellows and junior faculty who need to demonstrate productivity by completing and publishing research in a timely manner, secondary data analysis can be a key foundation to successfully starting a research career.
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