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A fixed-effect meta-analysis models the studies as estimating a common underlying treatment effect. In a generic inverse-variance version, independent log odds ratios $y_i$ have approximate distributions $N(\delta ,v_i)$ and are pooled with weights $1/v_i$. The supplied forest plot instead has the Mantel–Haenszel pooled odds ratio weights: for event/nonevent cells $a_i,b_i,c_i,d_i$ and total $n_i$, the normalized weights are proportional to $b_i{c}_i/n_i$. They reproduce the printed percentages and yield a pooled odds ratio about $2.338$. This is still a common-effect analysis; the two weighting methods should not be silently identified.

A random-effects meta-analysis allows different true effects: $y_i\mid {\delta }_i\sim N({\delta }_i,v_i)$ with ${\delta }_i\sim N(\mu ,{\tau }^2)$. The between-study heterogeneity variance ${\tau }^2$ adds to sampling variance, giving marginal variance $v_i+{\tau }^2$. Its pooled estimate targets the mean of the study-effect distribution rather than one identical effect. Uncertainty about heterogeneity must also be considered.

The I-squared statistic of $6.6\%$ is a small estimate of excess variation on the heterogeneity scale. It does not mean that $6.6\%$ of studies are different, nor that this percentage of patients responds differently. The Cochran's Q statistic compares the observed variation with that expected from the studies' sampling errors; its approximate null distribution has five degrees of freedom here. A heterogeneity-test p-value of $0.374$ provides no rejection of the common-effect hypothesis.

The individual confidence intervals in the forest plot overlap substantially, although some point estimates are larger than others. Taken together, there is little evidence of between-study heterogeneity in these six studies. Neither the nonsignificant test nor the small I-squared statistic proves equal effects: with only six studies, detecting moderate heterogeneity can be difficult. A study being individually significant while another is not is also not itself a test of their difference.