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Data extraction involves more than copying words and numbers from the publication to a form.
Clinical domain, methodological, and statistical knowledge is needed to ensure the right information is captured.
Interpretation of published data is often needed.
What is reported is sometimes not what was carried out.
Data extraction and evaluation of risk of bias and of applicability typically occur at the same time.
On Data Extraction (II)
Data Extraction: A Boring Task? “ It is an eye-opening experience to attempt to extract information from a paper that you have read carefully and thoroughly understood only to be confronted with ambiguities, obscurities, and gaps in the data that only an attempt to quantify the results reveals.” — Gurevitch and Hedges (1993) Gurevitch J, Hedges LV. In: Design and analysis of ecological experiments ; 1993.
“ Data for the 40 patients who were given all 4 doses of medications were considered evaluable for efficacy and safety. The overall study population consisted of 10 (44%) men and 24 (56%) women, with a racial composition of 38 (88%) whites and 5 (12%) blacks.”
Let us extract the number of deaths in two study arms, at 5 years of followup . . .
Inconsistencies in Published Papers
Results Text Overall Mortality […] 24 deaths occurred in the PCI group, […] and 25 in the MT group […] MED and MT = medical treatment; PCI = percutaneous coronary intervention PCI (205) MED (203) Dead 24 25
Berlin J, for the University of Pennsylvania Meta-analysis Blinding Study Group. Does blinding of readers affect the results of meta-analysis? Lancet 1997;350:185-6.
Green BF, Hall JA. Quantitative methods for literature reviews. Annu Rev Psychol 1984;35:37-53.
Gurevitch J, Hedges LV. Meta-analysis: combining the results of independent experiments. In: Scheiner AM and Gurevich J, eds. Design and analysis of ecological experiments . New York: Chapman & Hall; 1993. p. 347-70.
Source Forge Web site. Engauge Digitizer. Available at: http://sourceforge.net/projects/digitizer/files/Engauge%20Digitizer/.
Trikalinos TA, Ip S, Raman G, et al. Home Diagnosis of Obstructive Sleep Apnea-Hypopnea Syndrome . Technology Assessment (Prepared by Tufts–New England Medical Center Evidence-based Practice Center). Rockville, MD: Agency for Healthcare Research and Quality; August 2007. Available at: http://www.cms.gov/determinationprocess/downloads/ id48TA.pdf.
This presentation was prepared by Joseph Lau, M.D., and Thomas Trikalinos, M.D., Ph.D., members of the Tufts–New England Medical Center Evidence-based Practice Center, and Melissa L. McPheeters, Ph.D., M.P.H., and Jeff Seroogy, B.S., members of the Vanderbilt University Evidence-based Practice Center.
The information in this module is currently not included in Version 1.0 of the Methods Guide for Comparative Effectiveness Reviews (available at: http://www.effective healthcare.ahrq.gov/repFiles/2007_10DraftMethodsGuide.pdf).