title: Conducting high-value secondary dataset analysis: an introductory guide and resources.
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.
authors.affiliation: Department of Medicine, University of California, San Francisco, CA, USA. aksmith@ucsf.edu
authors.forename: Alexander K
authors.initials: AK
authors.lastname: Smith
authors.forename: John Z
authors.initials: JZ
authors.lastname: Ayanian
authors.forename: Kenneth E
authors.initials: KE
authors.lastname: Covinsky
authors.forename: Bruce E
authors.initials: BE
authors.lastname: Landon
authors.forename: Ellen P
authors.initials: EP
authors.lastname: McCarthy
authors.forename: Christina C
authors.initials: CC
authors.lastname: Wee
authors.forename: Michael A
authors.initials: MA
authors.lastname: Steinman
mesh_terms: D035843:Biomedical Research / Q000379:methods / Q000592:standards / Q000706:statistics & numerical data; D016208:Databases, Factual / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D020407:Internet / Q000592:standards; D017410:Practice Guidelines as Topic / Q000592:standards*