#Metafor vs comprehensive meta analysis mods#

H^2 (unaccounted variability / sampling variability): 911.72 I^2 (residual heterogeneity / unaccounted variability): 99.89% Tau (square root of estimated tau^2 value): 0.1088

Tau^2 (estimated amount of residual heterogeneity): 0.0118 (SE = 0.0033) Output: Mixed-Effects Model (k = 41 tau^2 estimator: REML)

#Metafor vs comprehensive meta analysis mods#

codes: 0 вЂ***вЂ™ 0.001 вЂ**вЂ™ 0.01 вЂ*вЂ™ 0.05 вЂ.вЂ™ 0.1 вЂ вЂ™ 1Д¬ode for moderator analysis (Where cat = categorical moderator, dic = dichotomous moderator and con = continuous moderator): res <- rma(yi, vi, mods = ~ Con1 + Con2 + Cat1 + Dic1 + Dic2 + Dic3 +Дic4 + Dic5 + Dic6, method='REML', data=Data) I^2 (total heterogeneity / total variability): 99.96% Tau (square root of estimated tau^2 value): 0.1105 Tau^2 (estimated amount of total heterogeneity): 0.0122 (SE = 0.0028) Output: Random-Effects Model (k = 41 tau^2 estimator: REML) Pred <- predict(res, transf=, targs=list(ni=Data$N))Дat.back <- summary(Data, transf=transf.ipft, ni=N) Res <- rma(yi, vi, method = 'REML', data=Data) Is this a good way of testing the moderators or should I have tested each moderator individually?Д¬ode for overall effect: Data <- escalc(measure='PFT', xi=Right, ni=N, data=Data)

I have added all the moderators into 1 model.

I have included both categorical/dichotomous moderators as well as continuous moderators, do I need to specify this somewhere?.

As the overall effect is calculated using transformations, and require a command to transform the effect size back to a proportion, should I do this as well for the moderators? (And if so, how?).

I am conducting a meta-analysis for proportions using the metafor-package in R and I have some trouble with the moderator analysis.Д«asically, there are three aspects I'm not sure about: