# How do you interpret a forest plot in meta-analysis?

## How do you interpret a forest plot in meta-analysis?

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 does a forest plot graph show?

What is a Forest Plot / Blobbogram? 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.

### Why is a heterogeneity test done in meta-analysis?

Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.

How do you interpret heterogeneity I2?

A rough guide to interpretation is as follows:

1. 0% to 40%: might not be important;
2. 30% to 60%: may represent moderate heterogeneity*;
3. 50% to 90%: may represent substantial heterogeneity*;
4. 75% to 100%: considerable heterogeneity*.

## What does heterogeneity mean in meta-analysis?

Heterogeneity in meta-analysis refers to the variation in study outcomes between studies. StatsDirect calls statistics for measuring heterogentiy in meta-analysis ‘non-combinability’ statistics in order to help the user to interpret the results.

Is forest plot 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.

### What is the dotted line in forest plot?

Forest plot of the studies reporting the association between ART use and high total cholesterol. The solid line on the Forest plot is the point of no effect (OR = 1) and the dashed line represents the overall pooled estimate.

Do you want heterogeneity in meta-analysis?

Heterogeneity is not something to be afraid of, it just means that there is variability in your data. So, if one brings together different studies for analysing them or doing a meta-analysis, it is clear that there will be differences found.

## What is a good I2 score?

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 determine if heterogeneity is present in forest plots?

In your forest plot, have a look at overlapping confidence intervals, rather than on which side your effect estimates are. Whether the results are on either side of the line of no effect may not affect your assessment of whether heterogeneity is present, but it may influence your assessment of whether the heterogeneity matters.

### What is a forest plot in meta analysis?

This is known as a meta-analysis. In this kind of study, we often see a graph, called a forest plot, which can summarise almost all of the essential information of a meta-analysis. Let’s find out how to read a forest plot. Pros and cons of a forest plot

How many types of graphs are there in a meta-analysis?

From each meta-analysis, the authors produced 11 types of graphs (box plot, weighted box plot, standardized residual histogram, normal quantile plot, forest plot, 3 kinds of funnel plots, trim-and-fill plot, Galbraith plot, and L’Abbé plot), and 3 reviewers assessed the resulting 1,100 plots.

## Can a forest plot be used as a source of bias?

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). However, it cannot display potential publication bias to readers.