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Data Extraction

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Data Extraction. Prepared for: The Agency for Healthcare Research and Quality (AHRQ), Training Modules for Systematic Reviews Methods Guide, www.ahrq.gov
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Data Extraction

This figure presents an overview of the steps in the systematic review process. The first step, the preparation of the topic, requires refinement of the topic and development of an analytic framework. The second step is to search for and select studies for inclusion, which involves identifying study eligibility criteria, searching for relevant studies, and selecting evidence for inclusion. The third step is to extract data, which involves extraction of data from individual studies. The fourth step is to analyze and synthesize studies, which involves assessing the quality of individual studies, assessing applicability, presenting findings in tables, synthesizing quantitative data, and grading the strength of evidence. The final step is to report the systematic review. This module focuses on data extraction.
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Systematic Review Process Overview

Keywords: systematic review process
Learning Objectives. 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
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Learning Objectives

Keywords: data extraction form | data extraction
Why Is Data Extraction Important? 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.
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Why Is Data Extraction Important?

Keywords: systematic review | meta-analysis | quality assessment | data extraction
On Data Extraction (I). 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 abstraction process: Is labor intensive. Can be costly and error prone. Different research questions may have different data needs.
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On Data Extraction (I)

Keywords: data extraction | publications | key questions | PICOTS
On Data Extraction (II). 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.
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On Data Extraction (II)

Keywords: data extraction | observational studies | risk of bias | applicability
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)
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Data Extraction: A Boring Task?

Keywords: data extraction | systematic review
Comparative Effectiveness Reviews: Clarifying Research Terminology (II). In the Evidence-based Practice Center Program, we often refer to two types of tables: Evidence Tables. Essentially are data extraction forms. Typically are study specific, with data from each study abstracted into a set of such tables. Are detailed and typically not included in main reports. Summary Tables. 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.
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Comparative Effectiveness Review: Clarifying Research Terminology (II)

Keywords: data extraction | evidence table | summary table
What Data To Collect? 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: Population. Intervention (or exposure). Comparator (when applicable). Outcome (remember numerical data). Timing. Study design (study setting).
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What Data To Collect?

Keywords: data extraction | key question | digitized graphs | population | intervention | comparator | outcome | study design | evidence table | PICOTS
Data Elements: Population, Intervention, and Comparator. 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 abstracted study. Study types include randomized trial, observational study, diagnostic test study, prognostic factor study, family-based or population-based genetic study, et cetera.
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Data Elements: Population, Intervention, and Comparator

Keywords: population | data extraction | intervention | comparator | PICOTS
Data Elements: Outcome (I). 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.
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Data Elements: Outcome (I)

Keywords: Technical Expert Panel | outcome
Data Elements: Outcome (II). 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). Survival data. Sensitivity, specificity, receiver operating characteristic. Correlations. Slopes.
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Data Elements: Outcome (II)

Keywords: meta-analysis | quantitative synthesis
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