Propensity score matching with clustered data: an application to birth register data

In this paper we consider the implementation of propensity score matching for clustered data. Different approaches to reduce bias due to cluster level confounders are considered: matching within clusters and random or fixed effects models for the estimation of the propensity score. All the methods are illustrated with an application to the estimation of the effect of caesarean section on the Apgar score using birth register data from Sardinia hospitals.