| tightenBlock-package {tightenBlock} | R Documentation |
Tightens an Observational Block Design by Balanced Subset Matching
Description
Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.
Details
The DESCRIPTION file:
| Package: | tightenBlock |
| Type: | Package |
| Title: | Tightens an Observational Block Design by Balanced Subset Matching |
| Version: | 0.1.7 |
| Authors@R: | c(person("Paul", "Rosenbaum", role = c("aut", "cre"), email = "rosenbaum@wharton.upenn.edu")) |
| Author: | Paul Rosenbaum [aut, cre] |
| Maintainer: | Paul Rosenbaum <rosenbaum@wharton.upenn.edu> |
| Description: | Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>). The main function is tighten(). The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package. |
| License: | GPL-2 |
| Encoding: | UTF-8 |
| LazyData: | true |
| Imports: | stats, MASS, rcbalance |
| Suggests: | rrelaxiv |
| Additional_repositories: | https://errickson.net/rrelaxiv/ |
| Depends: | R (>= 3.5.0) |
Index of help topics:
aHDLt Alcohol and HDL Cholesterol
addMahal Rank-Based Mahalanobis Distance Matrix
addNearExact Add a Near-exact Penalty to an Exisiting
Distance Matrix.
makematch Make a Match Using Two Criteria Matching with
Optimal Subset Matching
makenetwork Make the Network Used for Matching with Two
Criteria
startcost Initialize a Distance Matrix.
tighten Tightening an Observational Block Design
tightenBlock-package Tightens an Observational Block Design by
Balanced Subset Matching
Author(s)
Paul Rosenbaum [aut, cre]
Maintainer: Paul Rosenbaum <rosenbaum@wharton.upenn.edu>
References
Bertsekas, D. P., Tseng, P. (1988) <doi:10.1007/BF02288322> The relax codes for linear minimum cost network flow problems. Annals of Operations Research, 13, 125-190.
Rosenbaum, P. R., Ross, R. N. and Silber, J. H. (2007) <10.1198/016214506000001059> Minimum distance matched sampling with fine balance in an observational study of treatment for ovarian cancer. Journal of the American Statistical Association, 102(477), 75-83.
Rosenbaum, P. R. (2012) <doi:10.1198/jcgs.2011.09219> Optimal matching of an optimally chosen subset in observational studies. Journal of Computational and Graphical Statistics, 21(1), 57-71.
Rosenbaum, P. R. (2024) Tightening an observational block design to form an optimally balanced subdesign. Manuscript.
Zhang, B., D. S. Small, K. B. Lasater, M. McHugh, J. H. Silber, and P. R. Rosenbaum (2023) <doi:10.1080/01621459.2021.1981337> Matching one sample according to two criteria in observational studies. Journal of the American Statistical Association, 118, 1140-1151.
Examples
data(aHDLt)
result<-tighten(aHDLt,aHDLt$z,aHDLt$block,
x=cbind(aHDLt$age,aHDLt$education),
f=cbind(aHDLt$ibmi,(aHDLt$bmi>22.5)+(aHDLt$bmi>27.5)+(aHDLt$bmi>32.5)),
ncontrols=2)