Online Program

Large, Sparse Optimal Matching with Refined Covariate Balance in an Observational Study of the Health Outcomes Produced by New Surgeons

*Samuel D. Pimentel, University of Pennsylvania 
Rachel R. Kelz, University of Pennsylvania 
Jeffrey H. Silber, University of Pennsylvania/Children's Hospital of Philadelphia 
Paul R. Rosenbaum, University of Pennsylvania 

Keywords: Blocking, fine balance, near-fine balance, natural blocks, network optimization, observational study, optimal matching, sparse networks

Every newly trained surgeon performs a first unsupervised operation. How do her patients' health outcomes compare with the patients of experienced surgeons? A credible comparison must (1) occur within hospitals, since health outcomes vary widely by hospital; (2) compare outcomes of patients undergoing the same operative procedures, since the risks differ in a knee replacement and an appendectomy; (3) control for potentially higher risks among patients from the emergency room or from a less capable hospital, since new surgeons treat them at a disproportionate rate; and (4) compare patient samples with similar distributions of health problems such as diabetes. We introduce a new form of matching that pairs patients of 1252 new surgeons to patients of experienced surgeons, exactly balancing 176 surgical procedures and closely balancing 2.9 million finer patient categories. The new matching algorithm (which uses penalized network flows) exploits a sparse network to quickly optimize a match two orders of magnitude larger than usual in statistical matching. This allows extensive use of fine and near-fine balance constraints. The match was constructed in minutes using software in R.