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Systematic Reviews, a Guide: Data Extraction

What is PRISMA?

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Evidence-based minimum set of items for reporting in systematic reviews and meta-analyses.  Focused on randomized trials,  PRISMA can also be used as a basis for reporting systematic reviews of other types of research, particularly evaluations of interventions. Download the PRISMA Checklist

For more information on other protocols, see the Protocols page.

Data Extraction: PRISMA Item 10

The next step is for the researchers to read the full text of each article identified for inclusion in the review and extract the pertinent data using a standardized data extraction/coding form.  The data extraction form should be as long or as short as necessary and can be coded for computer analysis if desired.

If you are writing a narrative review to summarize information reported in a small number of studies then you probably don't need to go to the trouble of coding the data variables for computer analysis but instead summarize the information from the data extraction sheets for the included studies.

If you are conducting an analytical review with a meta-analysis to compare data outcomes from several clinical trials you may wish to computerize the data collection and analysis processes.  Elamin et al (2009) offer advice on how to decide what electronic tools to use to extract data for analytical reviews. The process of designing a coded data extraction form and codebook are described in Brown, Upchurch & Acton (2003) and Brown et al (2013).  You should assign a unique identifying number to each variable field so they can be programmed into fillable form fields in whatever software is used for data extraction/collection e.g. Adobe Acrobat Pro or Microsoft Access, to generate coded data that can be uploaded to analytical computer software such as Excel or SPSS.   

You might like to include on the data extraction form a field for grading the quality of the study, see the Screening for quality page for examples of some of the quality scales you might choose to apply.

Three examples of a data extraction form are below:

Study Characteristics: PRISMA Item 18

The data extraction forms can be used to produce a summary table of study characteristics that were considered important for inclusion. 

In the final report in the results section the characteristics of the studies that were included in the review should be reported for PRISMA Item 18 as:

  • Summary PICOS (Patient/Population, Intervention, Comparison if any, Outcomes, Study Design Type) and other pertinent characteristics of the reviewed studies should be reported both in the text in the Results section and in the form of a table. Here is an example of a table that summarises the characteristics of studies in a review, note this table could be improved by adding a column for the quality scoreyou assigned to the study. The summary table could either be an appendix or in the text itself if the table is small enough e.g. similar to Table 1 of Shah et al (2007).

A bibliography of the included studies should always be created, particularly if you are intending to publish your review. Read the advice for authors page on the journal website, or ask the journal editor to advise you on what citation format the journal requires you to use. 

Results: PRISMA Item 20

In the final report the results from individual studies should be reported for PRISMA Item 20 as follows:

For all outcomes considered (benefits or harms) from each included study write in the results section:

  • (a) simple summary data for each intervention group
  • (b) effect estimates and confidence intervals

In a review where you are reporting a binary outcome e.g. intervention vs placebo or control, report in the results section the combined effect outcome from your meta-analysis

Ideally represent the results from all studies reviewed on a “forest plot” summary diagram.  Here is an example of a forest plot, and on page 2 a description of how to interpret it.