tightenBlock-package: Tightens an Observational Block Design by Balanced Subset...
In tightenBlock: Tightens an Observational Block Design by Balanced Subset Matching

tightenBlock-package

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: This package was not yet installed at build time.
Index: This package was not yet installed at build time.

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)

tightenBlock documentation built on May 29, 2024, 1:16 a.m.