Best Practices for Data Extraction in Systematic Reviews

Data extraction is a critical step in the systematic review process, serving as the bridge between individual studies and synthesis. When done properly, it ensures that all relevant information is captured accurately and consistently. In this article, we'll explore best practices for efficient and reliable data extraction.

Planning Your Data Extraction

1. Develop a Comprehensive Data Extraction Form

Before beginning the extraction process, develop a detailed form that captures all the information needed to answer your research questions. Your form should include:

• Study characteristics (author, year, design, setting)
• Participant information (demographics, eligibility criteria)
• Intervention details
• Comparison treatments
• Outcome measures and definitions
• Results (effect sizes, confidence intervals)
• Risk of bias indicators

2. Pilot Test Your Form

Pilot test your extraction form on a small sample of studies to identify any issues or missing elements. This allows you to refine the form before committing to full data extraction.

3. Create a Coding Manual

Develop a coding manual with clear instructions on how to extract and code each variable. This improves consistency, especially when multiple reviewers are involved.

During Data Extraction

4. Use Independent Extraction by Multiple Reviewers

Having at least two reviewers independently extract data from each study is considered best practice. This reduces the risk of errors and bias in the extraction process.

5. Establish a Process for Resolving Disagreements

Define in advance how disagreements between reviewers will be resolved. Options include:

• Discussion to reach consensus
• Consulting a third reviewer
• Using predefined decision rules

6. Extract Data Directly from Original Sources

Whenever possible, extract data from original studies rather than reviews or summaries to avoid introduction of errors or bias.

7. Contact Study Authors When Necessary

Don't hesitate to contact study authors for clarification or additional data when important information is missing or unclear in the published report.

Leveraging Technology

8. Use Specialized Software

Evidence Table Builder and other specialized software can streamline the data extraction process by:

• Providing structured templates
• Facilitating collaboration among reviewers
• Tracking progress and managing workflow
• Reducing transcription errors
• Enabling direct export for analysis

9. Maintain Version Control

Keep track of all versions of your extraction form and dataset, especially when changes are made during the review process.

10. Document Extraction Decisions

Maintain detailed notes about any decisions made during extraction, particularly for complex or ambiguous data points.

Quality Control

11. Conduct Regular Consistency Checks

Periodically check for consistency in how data is being extracted, particularly for subjective judgments or complex variables.

12. Validate Extracted Data

After extraction is complete, validate a random sample of extracted data against original sources to ensure accuracy.

13. Address Missing Data Systematically

Develop a consistent approach for handling missing data, and document these decisions transparently.

Conclusion

High-quality data extraction is the foundation of a reliable systematic review. By following these best practices and using tools like Evidence Table Builder, researchers can ensure that their data extraction process is efficient, accurate, and transparent.

Investing time in planning and quality control during the data extraction phase pays dividends later, making synthesis and interpretation more straightforward and reducing the risk of errors that could undermine the validity of your review's conclusions.