Abstract
Generating forest plots is the next step after extracting data from studies eligible for meta-analysis. A forest plot displays the effect estimates and confidence intervals of individual studies and their meta-analysis. The key feature of the forest plot is the pooled effect estimate represented by the much sought after’diamond’. However, it is important to note that meta-analysis is not justified unless all potentially eligible studies have comparable clinical and methodological characteristics, and are addressing the question at the core of the systematic review. Failure to pay attention to this vital consideration leads to what is commonly called “garbage in and garbage out”. Assuring that the extracted data are in a suitable format and choosing the appropriate model (fixed effect vs random effects) for meta-analysis are other important considerations. This chapter is focussed on forest plots in a meta-analysis and provides a 10-point checklist for their assessment and interpretation.
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Patole, S. (2021). Forest Plots in a Meta-Analysis. In: Patole, S. (eds) Principles and Practice of Systematic Reviews and Meta-Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-71921-0_8
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