Metafor vs comprehensive meta analysis mods#
Rma (estimate, sei =stderror, mods = ~ meta, method = "FE", data =dat.comp, digits = 3 ) Fixed-Effects with Moderators Model (k = 2) We use a fixed-effects meta-regression model for this purpose, because the (residual) heterogeneity within each subset has already been accounted for by fitting random-effects models above. We can now compare the two estimates (i.e., the estimated average log risk ratios) by feeding them back to the rma() function and using the variable to distinguish the two estimates as a moderator. We also add a variable to distinguish the two models and, for reasons to be explained in more detail below, we add the estimated amounts of heterogeneity within each subset to the data frame.ĭat.comp <- ame (estimate = c ( coef (res1 ), coef (res2 ) ), stderror = c (res1$se, res2$se ), We then combine the estimates and standard errors from each model into a data frame. Res2 <- rma (yi, vi, data =dat, subset =alloc = "other" ) Res1 <- rma (yi, vi, data =dat, subset =alloc = "random" ) We will use the 'famous' BCG vaccine meta-analysis for this illustration.
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