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Comparison of Multivariate Matching Methods: Structures, Distances, and Algorithms
Xing Sam Gu and Paul R. Rosenbaum
Journal of Computational and Graphical Statistics
Vol. 2, No. 4 (Dec., 1993), pp. 405-420
Published by: Taylor & Francis, Ltd. on behalf of the American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of America
Stable URL: http://www.jstor.org/stable/1390693
Page Count: 16
You can always find the topics here!Topics: Simulations, Calipers, Observational studies, Covariance, Discriminants, Control units, High schools, Coordinate systems, Control groups, Epidemiologic bias
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A comparison and evaluation is made of recent proposals for multivariate matched sampling in observational studies, where the following three questions are answered: (1) Algorithms: In current statistical practice, matched samples are formed using "nearest available" matching, a greedy algorithm. Greedy matching does not minimize the total distance within matched pairs, though good algorithms exist for optimal matching that do minimize the total distance. How much better is optimal matching than greedy matching? We find that optimal matching is sometimes noticeably better than greedy matching in the sense of producing closely matched pairs, sometimes only marginally better, but it is no better than greedy matching in the sense of producing balanced matched samples. (2) Structures: In common practice, treated units are matched to one control, called pair matching or 1-1 matching, or treated units are matched to two controls, called 1-2 matching, and so on. It is known, however, that the optimal structure is a full matching in which a treated unit may have one or more controls or a control may have one or more treated units. Optimal 1 - k matching is compared to optimal full matching, finding that optimal full matching is often much better. (3) Distances: Matching involves defining a distance between covariate vectors, and several such distances exist. Three recent proposals are compared. Practical advice is summarized in a final section.
Journal of Computational and Graphical Statistics © 1993 American Statistical Association