Meta-analysis is a powerful tool to synthesize evidence from multiple studies on a common topic. But how do you communicate your meta-analysis results effectively and clearly? In this article, you'll learn about some easy ways to present meta-analysis results using graphs, tables, and text.
One of the most common and useful ways to present meta-analysis results is a forest plot. A forest plot is a graphical representation of the effect sizes and confidence intervals of each study included in the meta-analysis, as well as the overall pooled effect size and confidence interval. A forest plot can help you visualize the heterogeneity, consistency, and precision of the studies, as well as the direction and magnitude of the meta-effect. You can create a forest plot using software like RevMan, Stata, R, or Excel.
Another way to present meta-analysis results is a summary table. A summary table is a tabular summary of the main characteristics and outcomes of each study included in the meta-analysis, as well as the overall pooled effect size and confidence interval. A summary table can help you organize and compare the studies, as well as provide relevant details such as study design, sample size, quality, and risk of bias. You can create a summary table using software like RevMan, Stata, R, or Excel.
A third way to present meta-analysis results is a funnel plot. A funnel plot is a scatter plot of the effect sizes and standard errors of each study included in the meta-analysis. A funnel plot can help you assess the publication bias, which is the tendency for studies with positive or significant results to be more likely to be published than studies with negative or non-significant results. A funnel plot should show a symmetrical distribution of the studies around the pooled effect size, indicating no publication bias. If the funnel plot is asymmetrical, indicating publication bias, you can use methods like trim-and-fill or Egger's test to adjust the meta-effect. You can create a funnel plot using software like RevMan, Stata, R, or Excel.
A fourth way to present meta-analysis results is a text summary, which is a concise and clear description of the main findings and implications of the meta-analysis. This can be helpful for communicating the results to a wider audience, such as policymakers, practitioners, or the general public. The text summary should include the research question and objective of the meta-analysis, the number and type of studies included in the meta-analysis, the pooled effect size and confidence interval of the meta-analysis, the heterogeneity and publication bias of the meta-analysis, the strengths and limitations of the meta-analysis, and finally, the implications and recommendations of the meta-analysis.
Presenting meta-analysis results in the form of a code example can be beneficial for demonstrating methodology and reproducibility, as well as sharing code with other researchers. A code example should include the name and version of the software or programming language used, the data source and format used, the commands or functions used to perform the meta-analysis, the output or results of the meta-analysis, and comments or explanations of the code. For example, using R and the meta package, one can load data from a csv file, perform a random-effects meta-analysis, print a summary of the meta-analysis, plot a forest plot and funnel plot of the meta-analysis, and perform Egger's test for publication bias.
There are other options to present meta-analysis results, such as network meta-analysis, subgroup analysis, meta-regression, or sensitivity analysis. These options can help you explore the effects of different factors, such as interventions, populations, or moderators, on the meta-effect. However, these options are more complex and require more assumptions and interpretations. Therefore, you should use them with caution and justification, and always report them transparently and comprehensively.