Just as with the risk of bias assessment, data extraction should be conducted by two blinded, independent, reviewers to reduce bias and ensure data quality control.
A data extraction form should be developed early in the review planning process using the research question and inclusion criteria to customize the form to meet the needs of the project.
Commonly extracted fields for most SRs include:
To help develop the data extraction form, teams can use existing systematic reviews on their topic to identify what potential information to collect. If the team is planning to do a statistical analysis or meta-analysis they should consult with a biostatistician for advice on what numerical data should be collected.
Manuals, such as the Cochrane Handbook, provide starting templates for data extraction.
This form can be used as a guide for developing your own data extraction form. Sections can be expanded and added, and irrelevant sections can be removed.
National Academies Press/IOM Manual: Managing Data Collection
Software specifically build for doing systematic reviews like Covidence, have data extraction forms built in to them that can be adjusted to fit the team study and then can additionally assist in the comparison and arbitration in the event of different answers.
The review team should be trained on the data extraction form and what type of data would be expected for each category. Refer to the systematic review manuals to help your team establish standards. Then the team should pilot the extraction form using 5 to 10 randomly selected studies to ensure data extractors are recording similar data. Revise the extraction form if needed. To help with writing the manuscript and maintaining transparency in the process, document any changes to the process or the form and keep track of the decisions the team makes and the reasoning behind them.
