[R-meta] residual heterogeneity in meta-regression

[Daniel Mønsted Shabanzadeh]

Dear Wolfgang

I am performing a meta-regression on multiple one-arm non-randomised
studies in order to explore the impact of moderators on complications
following a surgical intervention. Adding moderators (age catrgory of the
patient, surgical technique etc.) increases the R2:

Mixed-Effects Model (k = 183; tau^2 estimator: REML)

tau^2 (estimated amount of residual heterogeneity):     0.0074 (SE = 0.0010)
tau (square root of estimated tau^2 value):             0.0860
I^2 (residual heterogeneity / unaccounted variability): 99.20%
H^2 (unaccounted variability / sampling variability):   125.63
R^2 (amount of heterogeneity accounted for):            36.78%

Test for Residual Heterogeneity:
QE(df = 156) = 4730.2255, p-val < .0001

Test of Moderators (coefficient(s) 2:27):
QM(df = 26) = 115.5671, p-val < .0001


However, I am unaware of how to interpretate the rising R2 when QE
tests keep on beeing significant. So far I understand that the rising R2
indicates that the heterogeneity is beeing partly explained by moderators,
however does QE change in case most heterogeneity is explained by
moderators?

Regards,
Daniel

Daniel Mønsted Shabanzadeh
MD, PhD
Department of Gastroenterology, Surgical Unit
Hvidovre Hospital
Mobile +45 2546 5251

	[[alternative HTML version deleted]]