• Alexander K Smith, John Z Ayanian, Kenneth E Covinsky, Bruce E Landon, Ellen P McCarthy, Christina C Wee, and Michael A Steinman.
• Department of Medicine, University of California, San Francisco, CA, USA. aksmith@ucsf.edu
• J Gen Intern Med. 2011 Aug 1; 26 (8): 920929920-9.

AbstractSecondary analyses of large datasets provide a mechanism 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 secondary 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.

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