I've completed multiple imputation of my dataset for the first time using the
mice package in R. I'm familiar with the procedure for using the
MatchIt package for propensity score matching using a normal dataframe. However, I'm not at all familiar with how to manipulate the
mids object which
mice outputs after imputation. I looked over a couple relevant threads but the questions being answered are more broad scope (I suppose my questions is more just about the code and functions used).
I don't know that
Matching are capable of making use of the
mids object, and I'm not sure how to bridge the divide. I could use
with.mids() to fit a model for the propensity score, but I'm uncertain how I would calculate the actual propensity scores using the
mira object and move on from there. Alternatively, I could output a long form dataframe with the imputed data sets stacked onto one another using
complete(imp, "long"), calculate propensity scores for each, average them, and complete matching. However, my outcome variable is imputed, so it's not clear that would be beneficial.