Question

In a meta-analysis, assessing heterogeneity (e.g. using the \(I^2\) statistic or Cochran's Q test):

• A. Evaluates the variation between the included studies
• B. Detects publication bias directly
• C. Measures confounding within a single study
• D. Quantifies the precision of an individual study

Hint:

Heterogeneity is about how much the studies differ from one another, not about bias.

Answer 1

The Correct Option is A

Step 1: Understand what heterogeneity means in a meta-analysis.
A meta-analysis statistically pools the results of several independent studies. Heterogeneity refers to the variability or differences in effect estimates between the included studies, beyond what is expected by chance alone.

Step 2: Tools used to assess it.
• Cochran's Q test - tests whether observed differences in results are due to chance.
• \(I^2\) statistic - quantifies the percentage of total variation across studies due to heterogeneity rather than chance (25% low, 50% moderate, 75% high).

Step 3: Map to the options.
Heterogeneity directly evaluates the variation between the included studies - option 1 is correct.

Step 4: Why the others are wrong.
• Publication bias is assessed separately, by a funnel plot or Egger's test, not by heterogeneity statistics.
• Confounding is a within-study validity issue, addressed at the individual-study level, not by between-study heterogeneity.
• The precision of an individual study is shown by its confidence interval / weight, not by heterogeneity.

Key fact: Heterogeneity (\(I^2\), Q) measures between-study variation; a high value favours a random-effects model.