In a forest plot of a published meta-analysis on antidiabetic therapy, small trials cluster symmetrically to the right of larger trials around the pooled estimate, leaving a visible gap where small negative trials should appear. The most appropriate conclusion is:

• A The confidence intervals of individual studies are artifactually narrow
• B The heterogeneity is exclusively due to clinical diversity
• C The large trials are methodologically superior and should be weighted out
• D The pooled estimate is inflated by publication bias and small-study effects ✓

Explanation

D funnel plot plots study precision against effect size; in its absence, small studies scatter symmetrically on both sides of the pooled estimate. Asymmetry with small positive trials present and small negative trials missing is the classic signature of publication bias, since negative small studies remain unpublished. Egger's test or trim-and-fill can formally assess it. Heterogeneity relates to spread between study estimates, not their distribution about the pooled value, and weighting by study size is inherent to meta-analysis regardless of direction of results.

Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.

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