I am working on a cardiovascular observational (i.e. non-randomized) study featuring three or more competing treatments.
My preference would be to conduct the analysis first using 1:1 propensity score matching, for instance using
MatchIt in R, or
psmatch2 in Stata. Then, confirm the main analysis without excluding any case by means of inverse probability of treatment weighting, for instance using
twang in R, or
meglm in Stata.
However, I have at least three groups under comparison, and so the standard appraoch needs rethinking.
I have found in the web the
TriMatch package for R, which appears to be capable to handle more than one treatment, but it seems to focus only on propensity scores.
In any case, what is the most sensible approach? Should I dichotomize all comparisons (eg destructuring an A vs B vs C comparison into A vs B, A vs C, and B vs C) and proceed with standard methods? Or should I pursue an alternative, and possibly more challenging, approach?