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J Gen Intern Med. 2011 Aug;26(8):920-9. doi: 10.1007/s11606-010-1621-5. Epub 2011 Feb 8.

Conducting high-value secondary dataset analysis: an introductory guide and resources.

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1
Department of Medicine, University of California, San Francisco, CA, USA. aksmith@ucsf.edu

Abstract

Secondary 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.

PMID:
21301985
PMCID:
PMC3138974
DOI:
10.1007/s11606-010-1621-5
[Indexed for MEDLINE]
Free PMC Article
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