Criteria often are unclear about which outcomes to include and which to discard. Outcomes should be determined a priori with the Technical Expert Panel. 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. Intervention or exposure and comparator items depend upon the extracted study. More specific items may be needed, depending upon the topic. Population-generic elements may include patient characteristics, such as age, gender distribution, and disease stage. 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) What Data To Collect? 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 extracted 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 Comparative Effectiveness Reviews: Clarifying Research Terminology In: Design and analysis of ecological experiments 1993. On Data Extraction (II)ĭata 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. Data extraction and evaluation of risk of bias and of applicability typically occur at the same time. What is reported is sometimes not what was carried out. Interpretation of published data is often needed. Clinical domain, methodological, and statistical knowledge is needed to ensure the right information is captured. 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?Įxtracted 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)ĭata extraction involves more than copying words and numbers from the publication to a form. 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 Learning Objectives Data Extraction Prepared for: The Agency for Healthcare Research and Quality (AHRQ) Training Modules for Systematic Reviews Methods Guide
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