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 summarise 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 forms 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. Reviewers can use fillable forms to collect and code data reported in the studies included in the review, the data can then be uploaded to analytical computer software such as Excel or SPSS for statistical analysis. GW School of Medicine, School of Public Health, and School of Nursing faculty, staff, and students can use the various statistical analytical software in the Himmelfarb Library, and watch online training videos from LinkedIn Learning at the Talent@GW website to learn about how to perform statistical analysis with Excel and SPSS.
Software to help you create coded data extraction forms from templates include: Covidence, DistillerSR (needs subscription), EPPI Reviewer (subscription, free trial), or AHRQ's SRDR tool (free) which is web-based and has a training environment, tutorials, and example templates of systematic review data extraction forms. If you prefer to design your own coded data extraction form from scratch 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 you decide to use for data extraction/collection. You can use AHRQ's Systematic Review Data Repository SRDR tool, or online survey forms such as Qualtrics, RedCAP, or Survey Monkey, or design and create your own coded fillable forms using Adobe Acrobat Pro or Microsoft Access. 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:
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:
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. Himmelfarb Library recommends using RefWorks to manage your references.
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:
In a review where you are reporting a binary outcome e.g. intervention vs placebo or control, and you are able to combine/pool results from several experimental studies done using the same methods on like populations in like settings, then in the results section you should report the relative strength of treatment effects from each study in your review and the combined effect outcome from your meta-analysis. For a meta-analysis of Randomized trials you should represent the meta-analysis visually on a “forest plot” (see fig. 2). Here is another example of a meta-analysis forest plot, and on page 2 a description of how to interpret it.
If your review included heterogenous study types (ie some combination of experimental trials and observational studies) you won't be able to do a meta-analysis, then instead your analysis could follow the Synthesis Without Meta-analysis (SWiM) guideline, and consider presenting your results in an alternative visually arresting graphic using a template in Excel or SPSS or from a web-based applications for infographics. GW faculty, staff, and students, may watch online training videos from LinkedIn Learning at the Talent@GW website to learn how to work with charts and graphs, and design infographics.