In metaanalysis results are often reported in forest plots, where multiple studies are included.

Different, continuous outcome scales are used to measure the effect of a treatment on the course of a specific disease. Because of heterogeneity in clinical studies, random effects models are used for the calculation of the forest plots. Results are reported as standardised mean difference (SMD), corresponding to the Cohens d. Estimations as to how big the cohens d has to be for beeing named as "big" or "small" effects.

Regarding the results of such a forest-plot I have the following questions:

1. Is it possible to draw another, maybe more practical, or concise result from a reported "cohens d", other than that the overall effect is "small" or "big" for example? Or does the cohens d stand for itself in statistics?
2. Is it possible to relate the reported effects to specific changes in the initially used outcome scales? Since the forest plot results are in a different form than the originally used outcome scales.

In metaanalysis results are often reported in forest plots, where multiple studies are included.

Different, continuous outcome scales are used to measure the effect of a treatment on the course of a specific disease. Because of heterogeneity in clinical studies, random effects models are used for the calculation of the forest plots. Results are reported as standardised mean difference (SMD), corresponding to the Cohens d. Estimations as to how big the cohens d has to be for beeing named as "big" or "small" effects.

Regarding the results of such a forest-plot I have the following questions:

1. Is it possible to draw another, maybe more practical, or concise result from a reported "cohens d", other than that the overall effect is "small" or "big" for example?

2. • a) Is it possible to relate the reported effects to specific changes in the initially used outcome scales? Since the forest plot results are in a different form than the originally used outcome scales.

• b) Is it possible to "recalculate" with the pooled effect (cohen d/SMD) the possible/theoretical changes for every included outcome measurement scale used in the single included studies? For example: The pooled effect of a metaanalysis is d=0,4 and the standard deviation of one of the included studies "A" is 5. Does this mean that the possible/theoretical effect on the used outcome scale would be a "2"? (The idea would be to just bring the results into a more comprehensible format for non statisticians)

In metaanalysis results are often reported in forest plots, where multiple studies are included.

Sometimes different, continuous outcome scales are used to measure the effect of a treatment on the course of a specific disease. Since clinical studies often have the problem of heterogeneity, random effects models are used for the calculation of the forest plots. Results are then reported as standardised mean difference (SMD), corresponding to the Cohens d.

There exist estimates as to how big the cohens d has to be for beeing regarded a "big or small effect".

To make metaanalysis results in general more accessible to a an audience without any deeper knowledge in statistics, the following questions arise:

1. Is it possible to draw another, maybe more practical, or concise result from a reported "cohens d", other than that the overall effect is "small" or "big" for example? Or does the cohens d stand for itself in statistics?
2. Is it possible to relate the reported effects to specific changes in the initially used outcome scales? Since the forest plot results are in a different form than the originally used outcome scales.

In metaanalysis results are often reported in forest plots, where multiple studies are included.

Different, continuous outcome scales are used to measure the effect of a treatment on the course of a specific disease. Because of heterogeneity in clinical studies, random effects models are used for the calculation of the forest plots. Results are reported as standardised mean difference (SMD), corresponding to the Cohens d. Estimations as to how big the cohens d has to be for beeing named as "big" or "small" effects.

Regarding the results of such a forest-plot I have the following questions:

1. Is it possible to draw another, maybe more practical, or concise result from a reported "cohens d", other than that the overall effect is "small" or "big" for example? Or does the cohens d stand for itself in statistics?
2. Is it possible to relate the reported effects to specific changes in the initially used outcome scales? Since the forest plot results are in a different form than the originally used outcome scales.