An R package for propensity score matching with clustered data

MatchIt and Matching are two main packages for propensity score analysis but currently there is not a package handling clustered data. Recently, several approaches to reduce bias due to cluster-level confounders were considered and compared using Monte Carlo simulations by Arpino and Cannas. These methods exploit the clustered structure of data in two ways: in the estimation of the propensity score model (through the inclusion of fixed or random effects) or in the implementation of the matching algorithm. We created R functions implementing the strategies described in Aprino and Cannas and we illustrate their use through the analysis of a clinical data set.