How do you read a forest plot chart?
Summary time
- Each horizontal line on a forest plot represents an individual study with the result plotted as a box and the 95% confidence interval of the result displayed as the line.
- The implication of each study falling on one side of the vertical line or the other depends on the statistic being used.
What do the squares mean on a forest plot?
confidence interval
In the forest plot the study is represented by a square and a horizontal line indicating the confidence interval, where the dimension of the square reflects the weight of each study. A solid vertical line usually corresponds to no effect of treatment.
What do you need to make a forest plot?
How to create a forest plot in Excel
- Create a clustered bar. First, highlight the first two columns containing the study name and the effect size.
- Add in the row positions.
- Add a scatter plot to your graph.
- Remove the clustered bar graph.
- Add error bars (whiskers) to the scatter points.
- Format the forest plot.
What does a high i2 mean?
The I^2 indicates the level of of heterogeneity. It can take values from 0% to 100%. If I^2 ≤ 50%, studies are considered homogeneous, and a fixed effect model of meta-analysis can be used. If I^2 > 50%, the heterogeneity is high, and one should usea random effect model for meta-analysis.
What are the parts of a forest plot?
Parts of a Forest Plot / Blobbogram A vertical line in the center. This is the line of no effect (or equality). If the blobbogram is a relative risk ratio, an effect size, or a mean difference, the line of no effect is at zero. For ratios (e.g. the odds ratio) the line is at 1.
When should I use a forest plot?
Forest plots are easy and straightforward to understand because they provide tabular and graphical information about estimates of comparisons or associations, corresponding precision, and statistical significance. This visual representation also makes it easier to see variations between individual study results.
What data do I need for a forest plot?
Additional data to the forest plot are (1) n, (2) mean and standard deviation, (3) P of each primary study, (4) meta-analysis overall 95% confidence interval, and (5) heterogeneity test result (with statistical significance level).
What is an acceptable I2?
While determining what constitutes a large I2 value is subjective, the following rule-of thumb can be used: < 40% may be low. 30-60% may be moderate. 50-90% may be substantial. 75-100% may be considerable.
What data do you need for a forest plot?
Why is it called forest plot?
At the September 1990 meeting of the breast cancer overview, Richard Peto jokingly mentioned that the plot was named after the breast cancer researcher Pat Forrest, and, at times, the name has been spelt “forrest plot.” However, the phrase actually originates from the idea that the typical plot appears as a forest of …
Are forest plots only for meta-analysis?
Forest Plots The forest plot is not necessarily a meta-analytic technique but may be used to display the results of a meta-analysis or as a tool to indicate where a more formal meta-analytic evaluation may be useful. An example of a forest plot is shown in Figure 4.
What does a high I2 value mean?
heterogeneity
The I^2 indicates the level of of heterogeneity. It can take values from 0% to 100%. If I^2 ≤ 50%, studies are considered homogeneous, and a fixed effect model of meta-analysis can be used. If I^2 > 50%, the heterogeneity is high, and one should usea random effect model for meta-analysis.
How do you know if a forest plot is statistically significant?
The statistical significance of a pooled estimate can be detected by visual inspection of the diamond (if the diamond width includes the line of no effect, there is no statistical difference between the two groups) or checking the p-value in the last row of a forest plot, “Test for overall effect” (P < 0.05 indicates a …
What is trim and fill?
The idea of the trim-and-fill method is to first trim the studies that cause a funnel plot’s asymmetry so that the overall effect estimate produced by the remaining studies can be considered minimally impacted by publication bias, and then to fill imputed missing studies in the funnel plot based on the bias-corrected …