rcbsubset-package: Optimal Subset Matching with Refined Covariate Balance
In rcbsubset: Optimal Subset Matching with Refined Covariate Balance
rcbsubset-package R Documentation
Optimal Subset Matching with Refined Covariate Balance
Description
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 rrelaxiv
package, which provides an alternative solver for
the underlying network flow problems, carries an
academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
Details
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.
By default the package uses the R package rlemon to solve the minimum-cost network flow optimization problems by which matches are computed. Alternatively, users may specify that the rrelaxiv package
should be used instead. However, this package carries an academic license
and is not available on CRAN so users must install it themselves.
Author(s)
Samuel D. Pimentel
Maintainer: Samuel D. Pimentel <spi@berkeley.edu>
References
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.
Rosenbaum, P.R. (2012) Optimal matching of an optimally chosen subset in observational studies, JCGS 21.1: 57-71.
rcbsubset documentation built on March 26, 2022, 1:08 a.m.
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| rcbsubset-package | R Documentation |
Optimal Subset Matching with Refined Covariate Balance
Description
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 rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.
Details
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.
By default the package uses the R package rlemon to solve the minimum-cost network flow optimization problems by which matches are computed. Alternatively, users may specify that the rrelaxiv package
should be used instead. However, this package carries an academic license
and is not available on CRAN so users must install it themselves.
Author(s)
Samuel D. Pimentel
Maintainer: Samuel D. Pimentel <spi@berkeley.edu>
References
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.
Rosenbaum, P.R. (2012) Optimal matching of an optimally chosen subset in observational studies, JCGS 21.1: 57-71.
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