Data Extraction Prepared for: The Agency for Healthcare Research and Quality (AHRQ) Training Modules for Systematic Reviews Methods Guide www.ahrq.gov
Systematic Review Process Overview
To describe why data extraction is important
To identify challenges in data extraction
To describe the general layout of a data extraction form
To suggest methods for collecting data accurately and efficiently
To discuss the pros and cons for querying original authors
To summarize studies in a common format to facilitate synthesis and coherent presentation of data
To identify numerical data for meta-analyses
To obtain information to assess more objectively the risk of bias in and applicability of studies
To identify systematically missing or incorrectly assessed data, outcomes that are never studied, and underrepresented populations
Why Is Data Extraction Important?
Extracted data should:
Accurately reflect information reported in the publication
Remain in a form close to the original reporting, so that disputes can be easily resolved
Provide sufficient information to understand the studies and to perform analyses
Extract only the data needed, because the extraction process:
Is labor intensive
Can be costly and error prone
Different research questions may have different data needs
On Data Extraction (I)
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.
In the Evidence-based Practice Center Program, we often refer to two types of tables:
Essentially are data extraction forms
Typically are study specific, with data from each study extracted into a set of such tables
Are detailed and typically not included in main reports
Are used in main reports facilitate the presentation of the synthesis of the studies
Typically contain context-relevant pieces of the information included in study-specific evidence tables
Address particular research questions
Comparative Effectiveness Reviews: Clarifying Research Terminology
Use key questions and eligibility criteria as a guide
Anticipate what data summary tables should include:
To describe studies
To assess outcomes, risk of bias, and applicability
To conduct meta-analyses
Use the PICOTS framework to choose data elements:
Intervention (or exposure)
Comparator (when applicable)
Outcome (remember numerical data)
Study design (study setting)
What Data To Collect?
Population-generic elements may include patient characteristics, such as age, gender distribution, and disease stage.
More specific items may be needed, depending upon the topic.
Intervention or exposure and comparator items depend upon the extracted study.
Study types include randomized trial, observational study, diagnostic test study, prognostic factor study, family-based or population-based genetic study, et cetera.
Data Elements: Population, Intervention, and Comparator
Outcomes should be determined a priori with the Technical Expert Panel.
Criteria often are unclear about which outcomes to include and which to discard.
Example: mean change in ejection fraction versus the proportion of subjects with an increase in ejection fraction by ≥5 percent
Record different definitions of “outcome” and consult with content experts before making a decision about which definition to use.
Data Elements: Outcome (I)
Apart from outcome definitions, quantitative data are needed for meta-analysis:
Dichotomous variables (e.g., deaths, patients with at least one stroke)
Count data (e.g., number of strokes, counting multiple ones)
Sample Fields From a Table Guidance Document: Vanderbilt University Evidence-based Practice Center
Example: Two Reviewers Extract Different Data Reviewer A Reviewer B
For evidence reports or technology assessments that have many key questions, data extraction forms may be several pages long.
The next few slides are examples of data extraction forms.
Remember, there is more than one way to structure a data extraction form.
Samples of Final Data Extraction Forms (Evidence Tables) Trikalinos TA, et al. AHRQ Technology Assessment. Available at: http://www.cms.gov/determinationprocess/downloads/id48TA.pdf .
Examples: Differential Data Extraction by Two Reviewers Trikalinos TA, et al. AHRQ Technology Assessment. Available at: http://www.cms.gov/determinationprocess/downloads/id48TA.pdf .
Characteristics of the Index Test and Reference Standard Trikalinos TA, et al. AHRQ Technology Assessment. Available at: http://www.cms.gov/determinationprocess/downloads/id48TA.pdf .
Results (Concordance/Accuracy) Trikalinos TA, et al. AHRQ Technology Assessment. Available at: http://www.cms.gov/determinationprocess/downloads/id48TA.pdf .
Results (Nonquantitative) Trikalinos TA, et al. AHRQ Technology Assessment. Available at: http://www.cms.gov/determinationprocess/downloads/id48TA.pdf .
Pencil and paper
Word processing software (e.g., Microsoft Word)
Spreadsheet (e.g., Microsoft Excel)
Database software (e.g., Microsoft Access, Epi Info™)
Dedicated off-the-shelf commercial software
Tools Available for Data Extraction and Collection
Who should extract the data?
Domain experts versus methodologists
What extraction method should be used?
Single or double independent extraction followed by reconciliation versus single extraction and independent verification
Should data extraction be blinded (to authors, journal, results)?
Extracting the Data Berlin J, for the University of Pennsylvania Meta-analysis Blinding Study Group. Lancet 1997;350:185-6.
Problems in data reporting
Inconsistencies in published papers
Data reported in graphs
Challenges in Data Extraction
“ 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.”
Examples of Data Reporting Problems (I)
Examples of Data Reporting Problems (II)
Examples of Data Reporting Problems (III)
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
Because so few research reports give effect size, standard normal deviates, or exact p-values, the quantitative reviewer must calculate almost all indices of study outcomes.
Little of this calculation is automatic, because results are presented in a bewildering variety of forms and are often obscure.
Why Do Such Problems Exist? Green BF, Hall JA. Annu Rev Psychol 1984;35:37-53.
Using Digitizing Software Source Forge Web site. Engauge Digitizer. Available at: http://sourceforge.net/projects/digitizer/files/Engauge%20Digitizer/.
Engauge Digitizer, an open-source software:
Each data point is marked with an “X,” and the coordinates are given in a spreadsheet.
Missing information in published papers
Publications with at least partially overlapping patient subgroups
Potentially fraudulent data
Additional Common Issues
Data extraction is laborious and tedious.
To err is human: data extractors will identify errors and will err themselves.
Interpretation and subjectivity are unavoidable.
Data are often not reported in a uniform manner (e.g., quality, location in paper, metrics, outcomes, numerical value vs. graphs).
Key questions will guide reviewers in choosing which information to extract.
There is no single correct way to record extracted data.
Extracting data requires familiarity with the content and knowledge of epidemiological principles and statistical concepts.
Often, one can extract more information than the paper initially appears to contain (e.g., by digitizing graphs).
Try to verify the same piece of information from different places in the same article.
Sometimes there are surprising inconsistencies.
Inconsistencies indicate suboptimal reporting quality at least.
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).