Forest plot

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

This meta-analysed measure of effect is commonly plotted as a diamond, the lateral points of which indicate confidence intervals for this estimate.

[2]: 252 The first use in print of the expression "forest plot" may be in an abstract for a poster at the Pittsburgh (US) meeting of the Society for Clinical Trials in May 1996.

[4] 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.

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.

Generic forest plot diagram
An example forest plot of five odds ratios (squares, proportional to weights used in meta-analysis), with the summary measure (centre line of diamond) and associated confidence intervals (lateral tips of diamond), and solid vertical line of no effect. Names of (fictional) studies are shown on the left, odds ratios and confidence intervals on the right.
This blobbogram uses seven studies to show that corticosteroids can hasten lung development in pregnancies where a baby is likely to be born prematurely . An odds ratio (OR) of one indicates no effect; studies with confidence intervals (horizontal lines) crossing one (vertical line) are inconclusive. Powerful studies (here, those with more participants ) have narrower (shorter) confidence intervals. A study with an odds ratio of one and a very narrow confidence interval would indicate no significant effect. Here the summary and the Auckland study have narrow confidence intervals that do not cross one, indicating that these studies would be judged statistically significant .