Abstract
Having constructed a matched control group, one must check that it is satisfactory, in the sense of balancing the observed covariates. If some covariates are not balanced, then adjustments are made to bring them into balance. Three adjustments are almost exact matching, exact matching, and the use of small penalties. Exact matching has a special role in extremely large problems, where it can be used to accelerate computation. Matching when some covariates have missing values is discussed.
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References
Avriel, M. : Nonlinear Programming. Englewood Cliffs, NJ: Prentice Hall (1976)
Cleveland, W.S. : The Elements of Graphing Data. Summit, NJ: Hobart Press.
Cochran, W.G.: The planning of observational studies of human populations (with Discussion). J Roy Statist Soc A 128, 234–265.
Cook, W.J., Cunningham, W.H., Pulleyblank, W.R., Schrijver, A.: Combinatorial Optimization. New York: Wiley (1998)
Hansen, B.B., Bowers, J. : Covariate balance in simple, stratified and clustered comparative studies. Statist Sci 23, 219–236 (2008)
Hansen, B.B.: The essential role of balance tests in propensity-matched observational studies. Statist Med 12, 2050–2054 (2008)
Imai, K., King, G., Stuart, E.A.: Misunderstandings between experimentalists and observationalists about causal inference. J Roy Statist Soc A 171, 481–502 (2008)
Karmanov, V.G.: Mathematical Programming. Moscow: Mir.
Marcus, S.M., Siddique, J., Ten Have, T.R., Gibbons, R.D., Stuart, E., Normand, S-L. T. Balancing treatment comparisons in longitudinal studies. Psychiatric Ann 38, 12 (2008)
Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)
Rosenbaum, P.R., Rubin, D.B.: Reducing bias in observational studies using subclassification on the propensity score. J Am Statist Assoc 79, 516–524 (1984)
Rosenbaum, P.R., Rubin, D.B. : Constructing a control group by multivariate matched sampling methods that incorporate the propensity score. Am Statist 39, 33–38 (1985)
Rosenbaum, P.R.: Optimal matching in observational studies. J Am Statist Assoc 84, 1024–1032 (1989)
Rosenbaum, P.R., Ross R.N., Silber, J.H. : Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. J Am Statist Assoc 102, 75–83. (2007)
Silber, J.H., Rosenbaum, P.R., Polsky, D., Ross, R.N., Even-Shoshan, O., Schwartz, S., Armstrong, K.A., Randall, T.C. : Does ovarian cancer treatment and survival differ by the specialty providing chemotherapy? J Clin Oncol 25, 1169–1175 (2007)
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Rosenbaum, P.R. (2010). Various Practical Issues in Matching. In: Design of Observational Studies. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-1213-8_9
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DOI: https://doi.org/10.1007/978-1-4419-1213-8_9
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