Tianjing Li, MD, MHS, PhD; S. Swaroop Vedula, MBBS, PhD; Nira Hadar, MS, PhD; Christopher Parkin, MS; Joseph Lau, MD; Kay Dickersin, MA, PhD
Disclaimer: The findings and conclusions of this article are those of the authors, who are responsible for its content, and do not necessarily represent the views of the Agency for Healthcare Research and Quality. No statement in this article should be construed as an official position of the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.
Grant Support: By the National Eye Institute, National Institutes of Health (grant 1 RC1 EY020140). The SRDR was initially developed by the Tufts University Evidence-based Practice Center and is now maintained by the Brown University Evidence-based Practice Center under contract with the Agency for Healthcare Research and Quality (contract no. HHSA 290-2007-10055-I and HHSA 290-2012-00012-I).
Disclosures: Dr. Li reports grants from the National Eye Institute during the conduct of the study. Dr. Vedula reports personal fees from Tufts University outside the submitted work. Ms. Hadar has nothing to disclose. Mr. Parkin has nothing to disclose. Dr. Lau reports grants from the Agency for Healthcare Research and Quality during the conduct of the study. Dr. Dickersin reports grants from the National Eye Institute during the conduct of the study and reports other from the National Eye Institute outside the submitted work. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOf InterestForms.do?msNum=M14-1603.
Requests for Single Reprints: Tianjing Li, MD, MHS, PhD, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, E6011, Baltimore, MD 21205; e-mail, firstname.lastname@example.org.
Current Author Addresses: Dr. Li: Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, E6011, Baltimore, MD 21205.
Dr. Vedula: Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, 200 Hackerman Hall, 3400 North Charles Street, Baltimore, MD 21218.
Ms. Hadar, Mr. Parkin, and Dr. Lau: Center for Evidence-based Medicine, Brown University School of Public Health, 121 South Main Street, Providence, RI 02912.
Dr. Dickersin: Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, E6152, Baltimore, MD 21205.
Author Contributions: Conception and design: T. Li, S.S. Vedula, N. Hadar, J. Lau.
Analysis and interpretation of the data: T. Li, K. Dickersin.
Drafting of the article: T. Li, S.S. Vedula, N. Hadar, C. Parkin, J. Lau.
Critical revision of the article for important intellectual content: T. Li, S.S. Vedula, J. Lau, K. Dickersin.
Final approval of the article: T. Li, S.S. Vedula, N. Hadar, J. Lau, K. Dickersin.
Provision of study materials or patients: T. Li, N. Hadar.
Statistical expertise: T. Li.
Obtaining of funding: K. Dickersin.
Administrative, technical, or logistic support: T. Li, S.S. Vedula, N. Hadar, J. Lau.
Collection and assembly of data: T. Li, N. Hadar, C. Parkin.
Li T, Vedula SS, Hadar N, Parkin C, Lau J, Dickersin K. Innovations in Data Collection, Management, and Archiving for Systematic Reviews. Ann Intern Med. 2015;162:287-294. doi: 10.7326/M14-1603
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Published: Ann Intern Med. 2015;162(4):287-294.
Data abstraction is a key step in conducting systematic reviews because data collected from study reports form the basis of appropriate conclusions. Recent methodological standards and expectations highlight several principles for data collection. To support implementation of these standards, this article provides a step-by-step tutorial for selecting data collection tools; constructing data collection forms; and abstracting, managing, and archiving data for systematic reviews. Examples are drawn from recent experience using the Systematic Review Data Repository for data collection and management. If it is done well, data collection for systematic reviews only needs to be done by 1 team and placed into a publicly accessible database for future use. Technological innovations, such as the Systematic Review Data Repository, will contribute to finding trustworthy answers for many health and health care questions.
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