If the point estimates land on the right side of the line of no effect and the confidence interval and diamond do not cross the line, the intervention improves the positive outcome. Statistics such as the heterogeneity (I-squared), overall effect size, and P-value are shown alongside a forest plot.
Full Answer
What is the 'no effect line' on a forest plot?
This key crossing point is typically represented on the forest plot using a vertical line, often referred to as the ‘no effect’ line. Forest plots show the ratio and confidence interval from each individual study using a box and horizontal line plot.
What is the interpretation of forest plots?
This table summarises the interpretation of forest plot when differences are used as effect measures. The interpretation is pretty much the same as for ratios. The only difference is that the line of no effect is at zero for differences while it is at 1 for ratios. 20
What do the horizontal lines on a forest plot represent?
Each horizontal line put onto a forest plot represents a separate study being analysed. In Figure 3, three studies are represented. Each study ‘result’ has two components to it:
Can a forest plot display publication bias to readers?
However, it cannot display potential publication bias to readers. A funnel plot can do that instead. Often, we have 6 columns in a forest plot. The leftmost column shows the identities (IDs) of the included studies.
What does the line of no effect mean in a forest plot?
The vertical line is the line of no effect (i.e. the position at which there is no clear difference between the intervention group and the control group). If the outcome of interest is adverse (e.g. mortality), the results to the left of the vertical line favour the intervention over the control.
How do you read effect size in forest plot?
The smaller the study, the wider the horizontal line and smaller the black box representing the point estimate. This means it is more likely those studies will cross the line of null effect (because your 95% confidence intervals will be much bigger).
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 does a forest plot show in meta-analysis?
The forest plot is able to demonstrate the degree to which data from multiple studies observing the same effect overlap with one another. Results that fail to overlap well are termed heterogeneous and is referred to as the heterogeneity of the data—such data is less conclusive.
How do you calculate effect size?
Generally, effect size is calculated by taking the difference between the two groups (e.g., the mean of treatment group minus the mean of the control group) and dividing it by the standard deviation of one of the groups.
What is the correct measure for determining an effect size for continuous data?
In the case of continuous data, Cohen's d is frequently used. This scales the difference between the means of two groups, or the mean difference between pairs of measurements, by dividing by the standard deviation.
How do you interpret the p-value in a forest plot?
7:3610:09Forest Plot Interpretation - Clearly Explained - YouTubeYouTubeStart of suggested clipEnd of suggested clipSince the effect sizes are standardized mean difference values the z statistic is reported. ThisMoreSince the effect sizes are standardized mean difference values the z statistic is reported. This value is used to determine the p-value for hypothesis testing in this case the p-value is 0.005 which
What does a forest plot graph show?
A blobbogram (sometimes called a forest plot) is a graph that compares several clinical or scientific studies studying the same thing. Originally developed for meta-analysis of randomized controlled trials, the forest plot is now also used for a variety of observational studies.
What is test for overall effect?
For tests of an overall effect, the computation of P involves both the effect estimate and precision of the effect estimate (driven largely by sample size). As precision increases, the range of plausible effects that could occur by chance is reduced.
How do you do a systematic review on a forest plot?
0:048:39Systematic Reviews Part 2: Forest Plots - YouTubeYouTubeStart of suggested clipEnd of suggested clipThe idea of a forest plot as many systematic reviews us the data from a systematic review isMoreThe idea of a forest plot as many systematic reviews us the data from a systematic review is summarized. In this thing called a forest plot and this is a forest plot you can see here.
How do you interpret meta-analysis results?
To interpret a meta-analysis, the reader needs to understand several concepts, including effect size, heterogeneity, the model used to conduct the meta-analysis, and the forest plot, a graphical representation of the meta-analysis.
What is a forest plot?
What a forest plot does, is take all the relevant studies asking the same question, identifies a common statistic in said papers and displays them on a single set of axis. Doing this allows you to compare directly what the studies show and the quality of that result all in one place.
What does each horizontal line on a forest plot represent?
Each horizontal line put onto a forest plot represents a separate study being analysed. In Figure 3, three studies are represented. Each study ‘result’ has two components to it:
What is the horizontal axis in a forest plot?
What you see to the left is the basic set of axes that forest plots employ. The horizontal axis usually represents the statistic the studies being profiled show. This could either be a ‘relative’ statistic like an odds ratio (OR) or a relative risk (RR). Or the statistics being used might be an ‘absolute’ one such as Absolute Risk Reduction (ARR) or Standardised Mean Difference (SMD). Knowing the difference between relative and absolute statistics is important because it affects which number sits at the vertical line.
What does the diamond at the bottom of a forest plot mean?
The diamond at the bottom of the forest plot shows the result when all the individual studies are combined together and averaged. The horizontal points of the diamond are the limits of the 95% confidence intervals and are subject to the same interpretation as any of the other individual studies on the plot.
What is a forest plot?
A forest plot is an essential tool to summarize information on individual studies, give a visual suggestion of the amount of study heterogeneity, and show the estimated common effect, all in one figure.
What is the common effect in a forest plot?
The common effects in the graphical part of the forest plot are drawn in the shape of diamonds; the widths of the diamonds indicate the confidence interval . The dotted vertical line is drawn at the value of the overall common effect.
What is vertical reference line?
A vertical reference line is typically plotted at the null hypothesis, with the statistical significance of an individual point and whiskers compared to that reference line. In cases where the data being compared are difference between means, the null is zero (0) and the x scales are normal.
Is a forest plot a meta-analysis?
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. Figure 4.
1. Included studies
The included studies are usually placed in the far-left of the plot. This contains a list of the studies represented by the first author of the publication and the year it was published.
2. Effect estimate information
Next to the list of the included studies, are extra information on the studies effect estimates. In the example, the effect estimate measured were odds ratios (ORs). So the extra information was the log OR with the standard error (SE), as well as the actual OR and the 95% confidence intervals (CIs).
3. Overall statistics
Underneath the list of the included studies with their extra information is the overall statistics. Within this, there are two statistics presented: heterogeneity and overall effect.
4. Forest plot
This is the actual forest plot itself. I have annotated the example further below.
What are the pros and cons of a forest plot?
Pros and cons of a forest plot. There are 3 main things we need to assess when reading a meta-analysis: Heterogeneity. The differences in the results, methodology or study populations used in the included studies. The pooled result.
Why are some studies missed?
Some studies can be missed because they are not written in English , or because they show non-significant results ( so they have a lower chance of being published). A forest plot does a great job in illustrating the first two of these (heterogeneity and the pooled result).
Statistical Significance
Interpreting The Box and Line Plot
- Forest plots show the ratio and confidence interval from each individual study using a box and horizontal line plot. The location of the box on the x-axis represents the ratio value for that outcome in that particular study, and the 95% confidence interval extends out as lines from the sides of this box. The x-axis of this plot is important, as it ...
Overall Comparison
- Finally, the overall comparison that takes the results of each study into account is shown beneath the individual study results in the shape of a diamond. There are no horizontal lines that extend out from this diamond; instead, the points of the diamond represent the limits of the confidence interval. If the diamond does not touch or cross the center line, the results of the meta-analysis i…
A Deeper Dive
- In addition to the visual content, forest plots typically show the numerical data supporting the figure. This can include the number of events that occurred in each group from each study (n/N), the actual percentage weighting value assigned to each study, the numerical value of the ratio and its confidence interval, and statistical test results for overall effect and heterogeneity. Often, yo…
References
- Gopalakrishnan S, Ganeshkumar P. Systematic reviews and meta-analysis: Understanding the best evidence in primary healthcare. J Fam Med Primary Care2013;2:9–14.