Tools for optimal subset matching of treated units and control units in observational studies, with support for refined covariate balance constraints, (including fine and near-fine balance as special cases). A close relative is the 'rcbalance' package. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel and Kelz (2020) <doi:10.1080/01621459.2020.1720693>.
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
This package computes matches that are optimal under a set of refined covariate balance constraints. These constraints, provided by the user, are a set of nested categorical variables of decreasing imporance which must be marginally balanced as closely as possible in the resulting treated and matched control populations. In addition, treated units may be excluded in an optimal manner (using a penalty parameter) to improve the quality of the match. For more detail see the references.
The main function is
rcbsubset, which takes a distance/sparsity matrix or matrix-like object containing information about matchability of the treated and control units and a list of fine balance variables and produces a match. The other functions are largely for internal use and should not be needed by the large majority of users. The syntax and code structure is very similar in the closely related antecedent package rcbalance, which provides more helper functions for constructing matches but does not support optimal subset matching.
IMPORTANT NOTE: the functionality of this package is greatly reduced unless the
optmatch package (v >= 0.9-1) is also loaded. When attempting to run the
rcbalance command without having loaded
optmatch, the users will receive an error message. The first reference below gives background on
Samuel D. Pimentel
Maintainer: Samuel D. Pimentel <firstname.lastname@example.org>
Hansen, B.B. and Klopfer, S.O. (2006) Optimal full matching and related designs via network flows, JCGS 15 609-627.
Pimentel, S.D., Kelz, R.R., Silber, J.H., and Rosenbaum, P.R. (2015) Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons, JASA 110 (510), 515-527.
Pimentel, S.D., and Kelz, R.R. (2020). Optimal tradeoffs in matched designs comparing US-trained and internationally trained surgeons. JASA 115 (532), 1675-1688.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.