I have a problem extremely similar to that found in question "Interaction effects in network meta-analysis (multiple treatment comparison) in R?", where I would like to use meta-analysis to investigate main and interaction factor effects of two to three factors on continuous outcome variables, where each study does not necessarily compare all potential factors together. In the previously posed question, the very helpful answer suggested assigning the factors as dummy variables (i.e. male, female became 1,0), and analyzing with the
rma.mv function in the metafor package.
I am doing a similar comparison, but with some factors that have greater than 2 levels, and am wondering how to incorporate these factors into the model without using a dummy variable, or with modified dummy variables. For example, comparing different genotypes (A,B,C) and food levels (x1, x2, x5) on weight gain. I.e. how to work into the model:
rma.mv(mean, vi, mods = ~ genotype*food, random = list(~ arm | study), data=my_data)
For food level, I think it would be possible to include this factor as (1, 2, 5) as they are scaled as such, but for genotype this would not be possible. One possibility would be to break genotype into two dummy variables (i.e. A or not A (1,0), B or not B (1,0) so that A B and C would be coded as (1,0) (0,1) and (0,0) respectively. However, I do not see how to work this into the model as a single factor, including interactions with food level. Is it possible to calculate the effects of multi-level factors and their interactions through meta-analysis, when not all factors/levels are included in each study? Thanks very much.