CODEWITHSUNDEEP
rsignificance
Svalf
Svalf

Reputation: 143

Forest plot interpretation

For my study I performed a meta-analysis of viral load measurements to test whether the specific interaction between A and B influences its levels.

This is the forest plot I obtained using R: MetaAnalysis.jpg

However, I don't know how to interpret it. I understand the this result is significant because p=0.0073 and because the overall effect estimate 95% CI does not overlap 0. However, what does it mean that the diamond is on the right side of the forest plot?

Upvotes: 1

Views: 923

Answers (1)

NRLP
NRLP

Reputation: 578

It depends on how the individual effect sizes are computed. This seems a forest plot of a meta-analysis of the correlations between A and B for each viral load (you indicate an association p-value). Perhaps you use the difference in the z-transformed correlations across the different viral loads and the associated standard deviations (?). If so, the way you compute this difference will help you interpret the overall effect size. Is it computed as value for large viral load minus value for small viral load? If so, the overall estimate shows that there is a larger effect of the interaction between A and B in the large viral loads. (If the diamond was on the left side of the vertical dotted line - i.e. the line of ‘no-effect’ - it would have reflected a larger interaction effect in small viral loads.)

One additional comment: you seem to estimate the overall effect size using random effects (incidentally, the sizes of the black squares for each individual study reflects the weight assigned to that study). The heterogeneity test seems non-significant (see heterogeneity p-value), meaning that heterogeneity does not affect the results of your meta-analysis. When this test turns out significant, you need to consider a mixed effects model (i.e. to find moderators in your dataset that help you explain this heterogeneity). Otherwise results are unreliable.

Upvotes: 1

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