Thursday, 7 January 2021

Interpretation of Forest Plot

Interpretation of Forest Plot

Study identities

Studies included in the meta-analysis and incorporated into the forest plot will be identified in chronological order on the left-hand side by author and date. There is no significance given to the vertical position assumed by a particular study.

Standardized mean difference

The chart portion of the forest plot will be on the right-hand side and will show the mean difference in effect between the test and control groups in the studies. A more precise rendering of the data shows up form in the text of each line, while a less precise graphic representation shows up in chart form on the right. The vertical line (y-axis) shows no effect. The horizontal distance of a box from the y-axis shows the difference between the test and control (the experimental data with control data subtracted out) concerning no observable effect, otherwise known as the magnitude of the experimental effect.

Confidence interval whiskers

The thin horizontal lines—sometimes referred to as whiskers—emerging from the box show the magnitude of the confidence interval. The longer the lines, the wider the confidence interval, and the less reliable the data. The shorter the lines, the narrower the confidence interval and the more reliable the data.

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

Weight

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.

Heterogeneity

The forest plot can show the degree to which data from multiple studies observing the same effect, overlap with one another. Results that cannot overlap well are termed heterogeneous and are referred to as the heterogeneity of the data—such data is less conclusive. If the results are similar between various studies, they say the data to be homogeneous, and the tendency is for these data to be more conclusive.

The heterogeneity is indicated by the I2. A heterogeneity of less than 50% is termed low and shows a greater similarity between study data than an I2 value above 50%, which shows more dissimilarity.

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