# Egger's regression test for funnel-plot asymmetry in a meta-analysis using metafor

My meta-analysis investigates the association between dietary intake of a micronutrient on the risk of being diagnosed with a disease (binary outcome).

I am trying to test for funnel-plot asymmetry of the studies I have included using an Egger's regression test, which is noted as the regtest() function in the metafor package.

Having read through the guide provided on the regtest() function , it appears I have an option of specifying either a 'weighted regression with multiplicative dispersion', which is a classical Egger's test, or a 'mixed-effects meta-regression model'.

I would appreciate if anyone could shed some light on which I should specify in my meta-analysis. Would the 'mixed-effects meta-regression model' be more appropriate for my meta-analysis which uses a random-effects model that accounts for between-study heterogeneity?

The provided reference Sterne and Egger (2005) in the guide explains that the first model may underestimate between-study heterogeneity, but I am not sure of the implication of this to my meta-analysis. I do get rather different p-values when I compare both methods.

I will share my outputs below.

> regtest(dietres,model="lm", predictor="sei") ##Classical Egger's Test

Regression Test for Funnel Plot Asymmetry

Model: weighted regression with multiplicative dispersion Predictor: standard error

Test for Funnel Plot Asymmetry: t = -2.0149, df = 17, p = 0.0600 Limit Estimate (as sei -> 0): b = 0.0515 (CI: -0.0493, 0.1524)

> regtest(dietres,model="rma", predictor="sei") ##mixed effects

meta-regression model

Regression Test for Funnel Plot Asymmetry

Model: mixed-effects meta-regression model Predictor: standard error

Test for Funnel Plot Asymmetry: z = -2.3660, p = 0.0180 Limit Estimate (as sei -> 0): b = 0.0683 (CI: -0.0333, 0.1700)