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Egger’s Test in Meta-Analysis

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

Funnel Plot and Egger's Regression Plot in Meta-analysis (FRCPath part 2 Medical Microbiology Question)
Funnel Plot and Egger's Regression Plot in Meta-analysis (FRCPath part 2 Medical Microbiology Question)

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

Funnel Plot: No Bias (Symmetrical)
Funnel Plot: No Bias (Symmetrical)


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


Funnel Plot: with bias (assymetrical)
Funnel Plot: with bias (assymetrical)

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)

Egger's Test
Egger's Test

📈 Visualising the regression

Visualising the Regression in Egger's Test
Visualising the Regression in Egger's Test
  • X-axis: precision (1/SE)

  • Y-axis: standardised effect

  • If the regression line is shifted away from zero, the intercept flags asymmetry.


Hypothesis testing in Egger's test
Hypothesis testing in Egger's test

⚠️ 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).


Worked example of Egger's Test
Worked example of Egger's Test


🎯 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


Practical considerations and Caveats: Egger's test
Practical considerations and Caveats: Egger's test

🧠 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)

Question 1 answer
Question 1 answer


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