I am doing meta analysis using "rma" (function) in the "metafor" package (in R). And this is my first time to do meta analysis.

The estimate is a proportion (# of patients with good outcome / Total # of patients).
Each paper has two groups to compare (Treatment 1 vs Treatment 2).
However, I do also know the average age, # of females, and location (country) of each paper.
I suspect that these three covariates affect the "proportion";
therefore I would like to adjust the effect from them.

In rma function, there is an argument called "mods", where I can put "moderator" variables. In such case, to adjust covariates, can I use (something like)

rma.da.adjusted <- rma(yi, vi, data = meta.dat, method = "DL",
mods = ~ age + female + location + treatment_group )


Here, age and female are continuous covariates, and location and treatment_group are categorical covariates.

• As long as they are appropriately coded (just like regression), yes. – Jeremy Miles Jun 30 '20 at 16:05
• Hi @JeremyMiles Thanks for the comment! So... if I use "mods = ~ treatment_group", then it means I am not considering any other covariates to adjust. However if I use "mods = ~ age + female + location + treatment_group", then am I considering demographic and geographic covariates to adjust? I hope I understand correctly. – rudgus51998 Jun 30 '20 at 22:55
• Yes. Note that these are at the study level though, not the individual person level. – Jeremy Miles Jun 30 '20 at 23:38
• @JeremyMiles I appreciate it!!! – rudgus51998 Jul 1 '20 at 1:34