Egger’s Test in Meta-Analysis
- FRCPath Prep Medical Microbiology Consultants

- Apr 21
- 4 min read
FRCPath Part 2 Microbiology Question
You are conducting a systematic review of probiotic therapy for C. difficile infection prevention. Your literature search identified 23 published studies, but your funnel plot shows clear asymmetry with missing studies in the bottom-left quadrant.
Meta-analysis Results:
Pooled OR: 0.65 (95% CI: 0.48-0.88)
I² = 68%
Egger's test: p = 0.03
Trim-and-fill analysis suggests 8 missing studies

1. What type of bias is suggested by these findings? (2 marks)
2. Describe three methods to investigate this bias further. (6 marks)
3. How would you adjust your conclusions based on these findings? (4 marks)
If you cannot answer this do not worry.
Let's start...
📊 Why this matters
When you pool studies in a meta-analysis, you’re aiming to estimate the true effect of an intervention or exposure. But the literature itself can be skewed: studies with “positive” or significant results are more likely to be published than those with null or negative findings. This publication bias can distort your conclusions.
Egger’s test is a widely used statistical method to detect asymmetry in a funnel plot—offering a quantitative check for potential bias.
🔍 Start with the funnel plot

A funnel plot displays:
X-axis: effect size (e.g., log odds ratio)
Y-axis: study precision (often 1/SE)
In an unbiased set of studies:
Large, precise studies cluster near the top
Smaller studies scatter more widely at the bottom
The overall shape is symmetrical (an inverted funnel)
⚠️ What bias looks like

When bias is present:
One side of the funnel is sparse or missing
Small studies may show inflated effects
The plot becomes asymmetrical
Visual inspection helps—but it’s subjective. That’s where Egger’s test comes in.
🧠 The core idea of Egger’s test
Egger’s test asks a simple, powerful question:
Do smaller studies show systematically different results than larger ones?
If they do, that pattern can create funnel plot asymmetry.
📐 How the test works (intuitively)
Each study’s effect is standardised (scaled by its standard error), then regressed against its precision:

🔑 Interpretation
Intercept ≈ 0 → no evidence of asymmetry
Intercept ≠ 0 (p < 0.05) → asymmetry → possible publication bias (or other small-study effects)

📈 Visualising the regression

X-axis: precision (1/SE)
Y-axis: standardised effect
If the regression line is shifted away from zero, the intercept flags asymmetry.

⚠️ Important caveats (don’t overclaim)
Egger’s test does not prove publication bias. It detects small-study effects, which can arise from:
Publication bias
Poorer methodology in smaller studies
True clinical heterogeneity
Selective outcome reporting
Practical tips:
Use when you have ≥10 studies (low power otherwise)
Interpret alongside the funnel plot and clinical context
Consider complementary methods (e.g., trim-and-fill)
🧪 Worked interpretation (quick example)
You run Egger’s test on 18 trials:
Intercept ( \beta_0 = 1.9 )
p = 0.01
Conclusion: Evidence of funnel plot asymmetry. This suggests small-study effects—possibly publication bias—but requires contextual interpretation (heterogeneity, study quality, outcomes).

🎯 Take-home points
Funnel plot = visual check for symmetry
Egger’s test = statistical test of that symmetry
Focus on the intercept: deviation from zero indicates asymmetry
Always interpret with caution and context

🧠 One-line summary
Egger’s test is a regression-based method that detects funnel plot asymmetry by testing whether the intercept differs from zero, indicating small-study effects suggestive of publication bias.
Based on above you should be able to answer question 1. What type of bias is suggested by these findings? (2 marks)

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