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 Learning Objectives
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 Why Is Data Extraction Important?
To identify systematically missing or incorrectly assessed data, outcomes that are never studied, and underrepresented populations
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: Can be costly and error prone On Data Extraction (I)
Different research questions may have different data needs
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. On Data Extraction (II)
Data extraction and evaluation of risk of bias and of applicability typically occur at the same time.
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 Comparative Effectiveness Reviews: Clarifying Research Terminology
Address particular research questions
Use key questions and eligibility criteria as a guide Anticipate what data summary tables should include: To assess outcomes, risk of bias, and applicability Use the PICOTS framework to choose data elements: Intervention (or exposure) Comparator (when applicable) Outcome (remember numerical data) What Data To Collect?
Study design (study setting)
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. Data Elements: Population, Intervention, and Comparator
Study types include randomized trial, observational study, diagnostic test study, prognostic factor study, family-based or population-based genetic study, et cetera.
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 Data Elements: Outcome (I)
Record different definitions of “outcome” and consult with content experts before making a decision about which definition to use.
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) Continuous variables (e.g., mm Hg, pain score) Data Elements: Outcome (II)
Sensitivity, specificity, receiver operating characteristic
The data elements to be extracted vary by type of study. Consider collecting this information when recording study characteristics for randomized trials: Number of centers (multicenter studies) Method of randomization (adequacy of allocation concealment) Data Elements: Timing and Study Design
Whether or not an intention-to-treat analysis was used
Provide “operational definitions” (instructions) indicating exactly what should be extracted in each field of the form. Make sure that all data extractors understand the operational definitions the same way. Pilot-test the forms on several published papers. Always Provide Instructions
Encourage communication to clarify even apparently mundane questions.
Independent extraction of data by at least two experienced reviewers is ideal but is also resource intensive. There is a tradeoff between cost and the quality of data extraction. Data extraction often takes longer than 2 hours per paper. A reduction in the scope of the work may be necessary if independent data extraction is desired. Single Versus Double Extraction
Careful single extraction by experienced reviewers, with or without crosschecking of selected items by a second reviewer, is a good compromise.
To address all needs, a generic data extraction form will have to be very comprehensive. Although there are common generic elements, forms need to be adapted to each topic or study design to be most efficient. Organization of information in the PICOTS (population, intervention, comparator, outcome, timing, and setting) format is highly desirable. Balance the structure of the form with the flexibility of its use. Anticipate the need to capture unanticipated data. Developing Data Extraction Forms (Evidence Tables)
Use an iterative process and have several individuals test the form on multiple studies.
Forms have to be constructed before any serious data extraction is underway. Original fields may turn out to be inefficient or unusable when coding begins. be as thorough as possible in the initial set-up, reconfigure the tables as needed, and Common Problems Encountered When Creating Data Extraction Forms (Evidence Tables) (I)
use a dual review process to fill in gaps.
Evidence Tables: Example (I) First Draft Second Draft
Evidence Tables: Example (II) Final Draft
Lack of uniformity among outside reviewers: No matter how clear and detailed are the instructions, data will not be entered identically by one reviewer to the next. Develop an evidence table guidance document—instructions on how to input data. Common Problems Encountered When Creating Data Extraction Forms (Evidence Tables) (II)
Limit the number of core members handling the evidence tables to avoid discrepancies in presentation.
In the “country, setting” field, data extractors should list possible settings that could be encountered in the literature: Academic medical center(s), community, database, tertiary care hospital(s), specialty care treatment center(s), substance abuse center(s), level I trauma center(s), et cetera. In the “study design” field, data extractors should list one of the following: Sample Fields From a Table Guidance Document: Vanderbilt University Evidence-based Practice Center
Randomized controlled trial, cross-sectional study, longitudinal study, case-control study, et cetera.
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. 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 .
Remember, there is more than one way to structure a data extraction form.
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 .
Word processing software (e.g., Microsoft Word) Spreadsheet (e.g., Microsoft Excel) Database software (e.g., Microsoft Access, Epi Info™) Tools Available for Data Extraction and Collection
Dedicated off-the-shelf commercial software
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 Extracting the Data Berlin J, for the University of Pennsylvania Meta-analysis Blinding Study Group. Lancet 1997;350:185-6.
Should data extraction be blinded (to authors, journal, results)?
Problems in data reporting Challenges in Data Extraction
Inconsistencies in published papers
Examples of Data Reporting Problems (I)
“ 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 (II)
Examples of Data Reporting Problems (III)
Inconsistencies in Published Papers
Let us extract the number of deaths in two study arms, at 5 years of followup . . .
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
Overall Mortality Figure MT = medical treatment PCI = percutaneous coronary intervention PCI (205) MT (203) Dead 24 25 28 35
Clinical Events Table CABG = coronary artery bypass graft MT = medical treatment PCI = percutaneous coronary intervention PCI (205) MT (203) Dead 24 28 32 25 35 33
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. Why Do Such Problems Exist? Green BF, Hall JA. Annu Rev Psychol 1984;35:37-53.
Little of this calculation is automatic, because results are presented in a bewildering variety of forms and are often obscure.
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 Additional Common Issues
Potentially fraudulent data
Data extraction is laborious and tedious. To err is human: data extractors will identify errors and will err themselves. Interpretation and subjectivity are unavoidable. Conclusions
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. Key Messages
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. References (I)
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. Authors
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).