Basic Tools of Multivariate Matching
The basic tools of multivariate matching are introduced, including the propensity score, distance matrices, calipers imposed using a penalty function, optimal matching, matching with multiple controls and full matching. The tools are illustrated with a tiny example from genetic toxicology (n = 46), an example that is so small that one can keep track of individuals as they are matched using different techniques.
KeywordsPropensity Score Mahalanobis Distance Pair Match Potential Control Treated Subject
Unable to display preview. Download preview PDF.
- Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Upper Saddle River, NJ: Prentice Hall (1993)Google Scholar
- Bergstralh, E.J., Kosanke, J.L., Jacobsen, S.L.: Software for optimal matching in observational studies. Epidemiology 7, 331–332\ (1996)Google Scholar
- Braitman, L.E., Rosenbaum, P.R.: Rare outcomes, common treatments: Analytic strategies using propensity scores. Ann Intern Med 137, 693–695 (2002)Google Scholar
- Cochran, W.G.: The planning of observational studies of human populations (with Discussion). J Roy Statist Soc A 128, 234–265.Google Scholar
- Costa, M., Zhitkovich, A., Toniolo, P.: DNA-protein cross-links in welders: Molecular implications. Cancer Res 53, 460–463 (1993)Google Scholar
- Fleiss, J.L., Levin, B., Paik, M.C.: Statistical Methods for Rates and Proportions. New York: Wiley (2001)Google Scholar
- Hansen, B.B.: Optmatch: Flexible, optimal matching for observational studies. R News 7, 18–24 (2007)Google Scholar
- Karmanov, V.G.: Mathematical Programming. Moscow: Mir.Google Scholar
- Mahalanobis, P.C.: On the generalized distance in statistics. Proc Natl Inst Sci India 12, 49–55 (1936)Google Scholar
- Maindonald, J., Braun, J.: Data Analysis and Graphics Using R. New York: Cambridge University Press (2005)Google Scholar
- R Development Core Team.: R: A Language and Environment for Statistical Computing. Vienna: R Foundation, http://www.R-project.org (2007)