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

REFERENCES

• Arias, E. (2012), “US Life Tables, 2008,” National Vital Statistics Reports, 61, 3, 9/24.
   Google Scholar
• Arratia, R., Goldstein, L., and Gordon, L. (1990), “Poisson Approximation and the Chen-Stein Method” (with discussion), Statistical Science, 5, 403–434.
   Google Scholar
• Baiocchi, M., Small, D.S., Yang, L., Polsky, D., and Groeneveld, P.W. (2012), “Near/Far Matching,” Health Services and Outcomes Research Methodology, 12, 237–253.
   Google Scholar
• Bauer, P., and Kieser, M. (1996), “A Unifying Approach for Confidence Intervals and Testing of Equivalence and Difference,” Biometrika, 83, 934–937.
   Web of Science ®Google Scholar
• Bertsekas, D.P. (1991), Linear Network Optimization, Cambridge, MA: MIT Press.
   Google Scholar
• Cook, W.J., Cunnigham, W.H., Pulleyblank, W.R., and Schrijver, A. (1998), Combinatorial Optimization, New York: Wiley.
   Google Scholar
• Diestel, R. (2010), Graph Theory (4th ed.), New York: Springer.
   Google Scholar
• Fisher, R.A. (1935), Design of Experiments, Edinburgh: Oliver & Boyd.
   Google Scholar
• Goeman, J.J., Solari, A., and Stijnen, T. (2010), “Three-Sided Hypothesis Testing,” Statistics in Medicine, 29, 2117–2125.
   PubMed Web of Science ®Google Scholar
• Hansen, B.B. (2007), “Optmatch,” R News, 7, 18–24. (Package optmatch in R)
   Google Scholar
• ——— (2008), “Prognostic Analogue of the Propensity Score,” Biometrika, 95, 481–488.
   Web of Science ®Google Scholar
• Hansen, B.B., and Klopfer, S.O. (2006), “Optimal Full Matching and Related Designs via Network Flows,” Journal of Computational and Graphical Statistics, 15, 609–627.
   Web of Science ®Google Scholar
• Heller, R., Manduchi, E., and Small, D.S. (2009), “Matching Methods for Observational Microarray Studies,” Bioinformatics, 25, 904–909.
   PubMed Web of Science ®Google Scholar
• Keele, L., Titiunik, R., and Zubizarreta, J.R. (2015), “Enhancing a Geographic Regression Discontinuity Design Through Matching to Estimate the Effect of Ballot Initiatives on Voter Turnout,” Journal of the Royal Statistical Society, Series A, 178, 223–229.
   Google Scholar
• Kiefer, J., and Wolfowitz, J. (1956), “Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters,” Annals of Mathematical Statistics, 27, 887–906.
   Google Scholar
• Korte, B., and Vygen, J. (2008), Combinatorial Optimization, New York: Springer.
   Google Scholar
• Lu, B., Greevy, R., Xu, X., and Beck, C. (2011), “Optimal Nonbipartite Matching and its Statistical Applications,” The American Statistician, 65, 21–30. (R package nbpmatching)
   PubMed Web of Science ®Google Scholar
• Neuman, M.D., Rosenbaum, P.R., Ludwig, J.M., Zubizarreta, J.R., and Silber, J.H. (2014), “Anesthesia Technique, Mortality, and Length of Stay After Hip Fracture Surgery,” Journal of the American Medical Association, 311, 2508–2517.
   PubMed Web of Science ®Google Scholar
• Neyman, J. (1923, 1990), “On the Application of Probability Theory to Agricultural Experiments,” Statistical Science, 5, 463–480.
   Google Scholar
• Rosenbaum, P.R. (1989), “Optimal Matching for Observational Studies,” Journal of the American Statistical Association, 84, 1024–1032.
   Web of Science ®Google Scholar
• ——— (2002), “Attributing Effects to Treatment in Matched Observational Stu-dies,” Journal of the American Statistical Association, 97, 183–192.
   Web of Science ®Google Scholar
• ——— (2010), Design of Observational Studies, New York: Springer.
   Google Scholar
• Rosenbaum, P.R., Ross, R.N., and Silber, J.H. (2007), “Minimum Distance Matched Sampling With Fine Balance in an Observational Study of Treatment for Ovarian Cancer,” Journal of the American Statistical Association, 102, 75–83.
   Web of Science ®Google Scholar
• Rosenbaum, P.R., and Rubin, D.B. (1985), “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score,” The American Statistician, 39, 33–38.
   Web of Science ®Google Scholar
• Rosenbaum, P.R., and Silber, J.H. (2009a), “Amplification of Sensitivity Analysis in Observational Studies,” Journal of the American Statistical Association, 104, 1398–1405. (amplify function in R package sensitivitymv)
   PubMed Web of Science ®Google Scholar
• ——— (2009b), “Sensitivity Analysis for Equivalence and Difference in an Observational Study of Neonatal Intensive Care Units,” Journal of the American Statistical Association, 104, 501–511.
   Web of Science ®Google Scholar
• Rubin, D.B. (1974), “Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies,” Journal of Educational Psychology, 66, 688–701.
   Web of Science ®Google Scholar
• Silber, J.H., Rosenbaum, P.R., Clark, A.S., et al. (2013), “Characteristics Associated With Differences in Survival Among Black and White Women With Breast Cancer,” Journal of the American Medical Association, 310, 389–397.
   PubMed Web of Science ®Google Scholar
• Stuart, E.A. (2010), “Matching Methods for Causal Inference,” Statistical Science, 25, 1–21.
   PubMed Web of Science ®Google Scholar
• Traskin, M., and Small, D.S. (2011), “Defining the Study Population for an Observational Study to Ensure Sufficient Overlap: A Tree Approach,” Statistics in Biosciences, 3, 94–118.
   Google Scholar
• Yang, D., Small, D.S., Silber, J.H., and Rosenbaum, P.R. (2012), “Optimal Matching With Minimal Deviation From Fine Balance in a Study of Obesity and Surgical Outcomes,” Biometrics, 68, 628–636.
   PubMed Web of Science ®Google Scholar
• Zubizarreta, J.R., Paredes, R.D., and Rosenbaum, P.R. (2014), “Matching for Balance, Pairing for Heterogeneity in an Observational Study of the Effectiveness of For-Profit and Not-For-Profit High Schools in Chile,” Annals of Applied Statistics, 8, 204–231.
   Web of Science ®Google Scholar
• Zubizarreta, J.R., Reinke, C.E., Kelz, R.R., Silber, J.H., and Rosenbaum, P.R. (2011), “Matching for Several Sparse Nominal Variables in a Case-Control Study of Readmission Following Surgery,” The American Statistician, 65, 229–238.
   PubMed Web of Science ®Google Scholar