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A forest plot can be used to compare the results of multiple medical studies. Often called a blobbogram, it generally consists of squares that indicate the estimated result of each study. Confidence levels of the results are usually shown by a horizontal line extending from either side of each square. The average value, and summation of the comparison, is typically indicated as a diamond on the bottom. Medical journals often feature a forest plot to convey comparisons of studies, and the graphical plots are supported by some types of word processing software.
Data referenced in a forest plot can indicate the effectiveness of a treatment, or its effect on mortality in a particular study, for example. Whether there are variations in the results can be shown, or a pattern of common findings may be determined. Researchers generally have a better understanding of data when it is plotted. They can also relay detailed information more clearly when such a graphical illustration is used. Individual expertise and clinical information can be combined and are often the basis for evidence based medicine, in which a forest plot is sometimes used for gauging the best treatments available.
On the bottom of the forest plot is typically a diamond. A vertical line extends from the diamond to show the overall effect of all the studies being compared. Another line is often used to show that no effect resulted from a clinical treatment. If this line crosses the confidence intervals of a particular study, this generally means that the results do not vary whether a treatment is performed or not.
The forest plot was first used in the 1970s, but every study was not combined to show an overall pattern. Plots that analyzed the outcome of various studies were developed in the 1980s, even though computer programs were generally unable to process them. The term forest plot began to be used in the 1990s in reference to the cluster of lines that appear in the analysis.
Computer software is often used in the 21st century to customize a forest plot. Users can choose symbols for a particular study, as well as to indicate the effect of all clinical trials being assessed. The sizes of symbols can be automatically calculated based on the importance of study results. Files are often exported to popular word processing programs, so researchers can present results to team members and other clinical professionals.
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