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The results of only two studies significantly differ from unity, as illustrated by the forest plot.
This was clearly visualized by the forest plot in Figure 2.
However, heterogeneity was low (I=12%) in the sensitivity analysis including only higher methodological quality studies, which confirms the small and similar incidence of major bleeding in both treatment arms already suggested by the forest plot that included all studies.
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We will show that clinically relevant subgroup effects can be explored in a simple manner by modifying the forest plot.
We determined heterogeneity between studies by: examining the forest plot (of relative risk [RR] for each trial) and using the I statistic.
The amount of variation between the collected effect sizes is shown together with the pooled estimates by the forest plots.
The consistency of the prognostic value across datasets is demonstrated by the forest plots in Figures 2c and 2d, where the results of the analysis of individual datasets are concisely summarized by the 5-year survival estimates and hazard ratios between the 'good' and 'poor' groups.
It is clear just by looking at the forest plot that the results are not consistent, but suggest a possible association.
Figure 1 shows, by way of example, the forest plot for the critical headway at single-lane roundabouts which provides context for the analysis of the set of studies [31].
Heterogeneity is assessed by the inspection of the forest plot, and using X tests with n-1 degree of freedom where n=number of studies involved.
Heterogeneity among the included studies will be assessed by visual inspection of the forest plot and a χ test with a significance level of p<0.10.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com