Forest plot

It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials.

The results of a meta-analysis are often shown in a forest plot.

The right-hand column is a plot of the measure of effect (e.g. an odds ratio) for each of these studies (often represented by a square) incorporating confidence intervals represented by horizontal lines.

It can be used to examine heterogeneity in a meta-analysis, as an alternative or supplement to a forest plot.

The Cochrane logo represents a meta-analysis of data from seven randomised controlled trials (RCTs), comparing one health care treatment with a placebo in a blobbogram or forest plot.

The blobbogram leading this article is from an iconic systematic review; it shows clinical trials of the use of corticosteroids to hasten lung development in pregnancies where a baby is likely to be born prematurely.

In the last twenty years, similar meta-analytical techniques have been applied in observational studies (e.g. environmental epidemiology) and forest plots are often used in presenting the results of such studies also.

It was developed for use in medical research as a means of graphically representing a meta-analysis of the results of randomized controlled trials. The left-hand column lists the names of the studies (frequently randomized controlled trials or epidemiological studies), commonly in chronological order from the top downwards.

The left-hand column lists the names of the studies (frequently randomized controlled trials or epidemiological studies), commonly in chronological order from the top downwards.

The graph may be plotted on a natural logarithmic scale when using odds ratios or other ratio-based effect measures, so that the confidence intervals are symmetrical about the means from each study and to ensure undue emphasis is not given to odds ratios greater than 1 when compared to those less than 1.

In September 1990, Richard Peto joked that the plot was named after a breast cancer researcher called Pat Forrest and as a result the name has sometimes been spelled "forrest plot".

This blobbogram is from an iconic medical review; it shows clinical trials of the use of corticosteroids to hasten lung development in pregnancies where a baby is likely to be born prematurely. After a systematic review made the evidence better-known, the treatment was used more, preventing thousands of pre-term babies from dying of infant respiratory distress syndrome.

This blobbogram is from an iconic medical review; it shows clinical trials of the use of corticosteroids to hasten lung development in pregnancies where a baby is likely to be born prematurely.

This blobbogram is from an iconic medical review; it shows clinical trials of the use of corticosteroids to hasten lung development in pregnancies where a baby is likely to be born prematurely.

After a systematic review made the evidence better-known, the treatment was used more, preventing thousands of pre-term babies from dying of infant respiratory distress syndrome.

Studies included in the meta-analysis and incorporated into the forest plot will generally be identified in chronological order on the left hand side by author and date.

The chart portion of the forest plot will be on the right hand side and will indicate the mean difference in effect between the test and control groups in the studies.

The vertical line (y-axis) indicates no effect.

The thin horizontal lines—sometimes referred to as whiskers—emerging from the box indicate the magnitude of the confidence interval.

If either the box or the confidence interval whiskers pass through the y-axis of no effect, the study data is said to be statistically insignificant.

The meaningfulness of the study data, or power, is indicated by the weight (size) of the box.

More meaningful data, such as those from studies with greater sample sizes and smaller confidence intervals, is indicated by a larger sized box than data from less meaningful studies, and they contribute to the pooled result to a greater degree.

Results that fail to overlap well are termed heterogeneous and is referred to as the heterogeneity of the data—such data is less conclusive.