Forest plot

. 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. This blobbogram 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. Long after there was enough evidence to show that this treatment saved babies' lives, the evidence was not widely known and the treatment was not widely used. 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. However, when the treatment was rolled out in lower- and middle-income countries, it was found that more pre-term babies died. It is thought that this may be because of the higher risk of infection, which is more likely to kill a baby in places with lower-quality medical care. A 2017 systematic review states that there is "little need" for further research into the usefulness of the treatment in higher-income countries, but further research is needed on how best to treat lower-income and higher-risk mothers, and optimal dosage. ==Reading a forest plot==

Study identities 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. 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 indicate the mean difference in effect between the test and control groups in the studies. A more precise rendering of the data shows up in number form in the text of each line, while a somewhat less precise graphic representation shows up in chart form on the right. The vertical line (y-axis) indicates no effect. The horizontal distance of a box from the y-axis demonstrates the difference between the test and control (the experimental data with control data subtracted out) in relation to 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 indicate 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, the study data is said 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 is able to demonstrate the degree to which data from multiple studies observing the same effect overlap with one another. Results that fail to overlap well are termed heterogeneous and is referred to as the heterogeneity of the data—such data is less conclusive. If the results are similar between various studies, the data is said 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 indicates a greater degree of similarity between study data than an I2 value above 50%, which indicates more dissimilarity. == See also ==