Tables and figures help to present included studies and their findings in a systematic and clear format.
Forest plots are the standard way to illustrate results of individual studies and meta-analyses. These can be generated using Review Manager software.
A ‘Summary of findings’ table provides key information concerning the quality of evidence, the magnitude of effect of the interventions examined, and the sum of available data on all important outcomes for a given comparison.
The abstract of a review should be focused primarily at decision makers (including clinicians, informed consumers and policy makers); and a ‘Plain language summary' conveys the findings in a style that can be understood by the general public.
The link to the Cochrane Handbook gives more detailed information on the above points.
In order to perform a meta-analysis you need to have a group of studies which are similar - this similarity is referred to as homogeneity. As a minimum, studies should be homogeneous in terms of participants, interventions, and outcomes so the meta-analysis can determine a meaningful conclusion.
Any kind of variability among studies is referred to as heterogeneity. According to Cochrane these are the kinds of heterogeneity:
It is important to measure and address heterogeneity in systematic reviews as it affects the extent to which conclusions can be relied on.
Qualitative data analysis software which enables you to handle rich text based information. It automates many manual tasks associated with analysis, like data classification and sorting. Available through the University.
Software used for statistical analysis. Available through the University.
Cochrane has a GRADE approach to rating the certainty of evidence in systematic reviews and other evidence syntheses. It also includes how to create a ‘Summary of findings’ table.
Key Points of the GRADE approach:
offers a transparent and structured process for developing and presenting summaries of evidence, including its quality, for systematic reviews and recommendations in health care
provides guideline developers with a comprehensive and transparent framework for carrying out the steps involved in developing recommendations
it’s use is appropriate and helpful irrespective of the quality of the evidence: whether high or very low