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"data extraction"

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Overview: Methods

Presentation: Clostridium difficile Infections: Diagnosis, Treatment, and Prevention
Keywords: analysis | data sources | literature | methods | overview
Systematic Review Process Overview. This figure presents an overview of the steps in the systematic review process. The first step, the preparation of the topic, requires refining the topic and developing 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 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 the development of analytic frameworks.
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Systematic Review Process Overview

Presentation: Analytic Frameworks
Keywords: systematic review | topic refinement | analytic framework | eligibility criteria | data extraction
This slide contains a table entitled Evidence Table Template for Applicability. The table consists of six columns and two rows. The first row is a header row containing the title headings for each column. Column one is titled Trial. Column two is titled population. Column three is titled intervention. Column four is titled comparator. Column five is titled outcomes, setting. Column six is titled comments. There is one data row. In the single data row, the contents of each cell are as follows: Column 1: citation Smith et al., followed by the number 24 as a superscript. Column 2: Heart failure population. Population is underlined. On the next line, below: Mean age: 65 years. 65 years is underlined. On the next line, below: NYHA class II or III: 83%. Column 3: Surgical debulking of myocardium. Column 4: Watchful waiting. (ACE inhibitor use, 34%; beta-blocker use, 40%. ) Column 5: Hospitalizations and survival. Survival is underlined. This is followed by: Median followup at 1 year. Year is underlined. This is followed on the next line below by the words Single, large, tertiary care hospital. Column 6: An efficacy trial (underlined;) limited standardization of intervention (underlined,) comparator did not include optimal medical therapy (underlined,) unclear how the benefits and harms would compare in a smaller community hospital.
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Evidence Table Template for Applicability

Presentation: Assessing Applicability
Keywords: PICOS | applicability | population | intervention | comparator | effectiveness trial | efficacy trial | PICOTS
Overview (2) Data sources: Twelve electronic databases (1990 to September 2009), grey literature, trial registries, and reference lists were searched. Study selection: Controlled and uncontrolled studies that assessed nonoperative or operative treatments or postoperative rehabilitation for adults with confirmed rotator cuff tears were included. Operative studies in English-language publications and nonoperative and postoperative rehabilitation studies in English, French, or German were considered. Studies were assessed in duplicate. Data extraction: Two reviewers assessed risk for bias by using the Cochrane tool and the Newcastle–Ottawa scale. One reviewer rated the evidence by using a modified GRADE (Grading of Recommendations Assessment, Development, and Evaluation) approach. Data were extracted by one reviewer and verified by another.
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Overview (2)

Presentation: Comparative Effectiveness of Nonoperative and Operative Treatments for Rotator Cuff Tears
Keywords: comparative effectiveness | nonoperative | operative | rotator cuff tear | data sources | study selection | data extraction
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

Presentation: 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

Presentation: Data Extraction
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

Presentation: Data Extraction
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?

Presentation: Data Extraction
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)

Presentation: Data Extraction
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)

Presentation: Data Extraction
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?

Presentation: Data Extraction
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)

Presentation: Data Extraction
Keywords: data extraction | evidence table | summary table
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