Data extraction forms are fundamental to systematic reviews, acting as the structured framework for gathering essential information from various research studies. These forms ensure consistency and accuracy in data collection, enabling researchers to effectively analyze, interpret, and synthesize findings from diverse sources. This article offers a comprehensive guide to designing robust data extraction forms for systematic reviews, encompassing key considerations, best practices, potential challenges, and practical recommendations.
| Source | Strengths | Limitations |
|---|---|---|
| Journal articles | Easily accessible, quick data extraction, often include useful information about methods and results | May not be available for all studies, potential for reporting biases, may contain limited study characteristics and methods, can omit outcomes, especially harms |
| Conference abstracts | Can help identify unpublished studies | Often include limited information about study design, may contain unclear information for meta-analysis, potential for double-counting studies if not correctly linked to other reports |
| Trial registrations | Can identify otherwise unpublished trials, may contain information about design, risk of bias, and results not included in other public sources, can link multiple reports of the same study | Limited to more recent studies that comply with registration requirements, often contain limited information about trial design and quantitative results, may report only harms occurring above a certain threshold, may be inaccurate or incomplete for trials whose methods have changed |
| Regulatory information | Can identify studies not reported in other public sources, may describe details of methods and results not found in other sources | Available only for studies submitted to regulators, available for approved indications but not 'off-label' uses, not always in a standard format, not often available for older products |
| Clinical study reports (CSRs) | Contain detailed information about study characteristics, methods, and results, can be particularly useful for identifying detailed information about harms, describe aggregate results which are easy to analyze | Do not exist or are difficult to obtain for most studies, require more time to obtain and analyze than public sources |
| Individual participant data | Allow for contemporary statistical methods and standardized analyses across studies, permit additional analyses (e.g., subgroup analyses) | Require considerable expertise and time to obtain and analyze, may lead to the same results as aggregate reports, may not be necessary if a CSR is available |
4. Choose a Data Extraction Tool
A variety of tools are available for creating data extraction forms, ranging from basic spreadsheets to specialized software. The choice of tool should be guided by the complexity of the review, the number of reviewers involved, and the available resources. Here's a comparison of common data extraction tools:
| Tool | Advantages | Disadvantages |
|---|---|---|
| Spreadsheets (e.g., Excel) | Simple to use, widely accessible, suitable for smaller reviews, functions like drop-down menus and range checks can help prevent data entry errors | Can become unwieldy for large reviews, limited functionality for complex data structures |
| Relational Databases (e.g., Microsoft Access) | Can handle complex datasets with multiple categories and relationships, offers greater flexibility and data management capabilities | Requires more technical expertise, may be more time-consuming to set up |
| Systematic Review Software (e.g., Covidence, DistillerSR, RevMan) | Offers specialized features for data extraction, risk of bias assessment, and data synthesis, some platforms provide templates and automated functions, can facilitate collaboration among reviewers | Can be expensive, may have limitations in terms of flexibility or customization |
It's important to note that complex systematic reviews, especially those involving multiple interventions or outcomes, may benefit from using relational databases. These databases can efficiently manage data with multiple dependencies, allowing for more sophisticated analyses and data management12.
5. Structure the Data Extraction Form
Organize the data extraction form in a clear and logical manner to facilitate efficient data entry and analysis. Use headings, subheadings, and clear labels for each data element. Consider grouping related elements together to improve readability and ensure that reviewers can easily locate the information they need13.
6. Provide Clear Instructions
Include detailed instructions for each data element, specifying how to extract and record the information. Clearly define any codes or abbreviations used to ensure consistency across reviewers and minimize the risk of misinterpretation13.
7. Pilot Test the Form
Before commencing the full data extraction process, it's crucial to pilot test the form on a small sample of included studies. This pilot testing helps identify any ambiguities, missing elements, or areas for improvement in the form's design and instructions. It also allows you to determine if any fields should be added or clarified to ensure the form captures all the necessary information5.
8. Train Reviewers
Provide thorough training to all reviewers involved in data extraction. Ensure they understand the instructions, coding system, and procedures for resolving any discrepancies that may arise during the extraction process. Training should also cover the use of the chosen data extraction tool and any relevant software features8.