Comparing different approaches for propensity score matching with clustered data: a simulation study

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• dc.contributor.author Arpino, Brunoca
• dc.contributor.author Cannas, Massimoca
• dc.date.accessioned 2017-04-05T10:49:47Z
• dc.date.available 2017-04-05T10:49:47Z
• dc.date.issued 2015-03
• dc.description.abstract This article focuses on the implementation of propensity score matching for clustered data. Different approaches to reduce bias due to cluster-level confounders are considered and compared using Monte Carlo simulations. We investigated methods that 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. In addition to a pure within-cluster matching, we also assessed the performance of a “preferential” within-cluster matching. This approach first searches for control units to be matched to treated units within the same cluster. If matching is not possible within-cluster, then the algorithm searches in other clusters. All considered approaches successfully reduced the bias due to the omission of a cluster-level confounder. The preferential within-cluster matching approach, combining the advantages of withinand between-cluster matching, showed a relatively good performance both in the presence of big and small clusters and it was often the best method. An important advantage of this approach is that it reduces the number of unmatched units as compared to a pure within-cluster matching. We applied these methods to the estimation of the effect of caesarean section on the Apgar score using birth register data.ca
• dc.format.mimetype application/pdfca
• dc.identifier.uri http://hdl.handle.net/10230/30795
• dc.language.iso engca
• dc.relation.ispartofseries RECSM Working Paper Series;43
• dc.rights This is an Open Access article distributed under the terms of the Creative Commons Attribution License Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properlyattributed.ca
• dc.rights.accessRights info:eu-repo/semantics/openAccessca
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/ca
• dc.subject.keyword Propensity score
• dc.subject.keyword Matching
• dc.subject.keyword Clustered data
• dc.subject.keyword Treatment effects
• dc.subject.keyword Caesarean section
• dc.subject.keyword Apgar score
• dc.title Comparing different approaches for propensity score matching with clustered data: a simulation studyca
• dc.type info:eu-repo/semantics/workingPaperca