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In submax: Effect Modification in Observational Studies Using the Submax Method
submax-package R Documentation
Effect Modification in Observational Studies Using the Submax
Method
Description
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
The main function is submax(). Also helpful is score(). See their documentation.
Author(s)
Paul R. Rosenbaum [aut, cre]
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
References
Lee, K., Small, D. S., & Rosenbaum, P. R. (2018) <doi:10.1111/biom.12884> A powerful approach to the study of moderate effect modification in observational studies. Biometrics, 74(4), 1161-1170.
Examples
#Reproduces parts of Table 2 of Lee et al. (2018)
data(Active)
submax(Active$delta,Active[,1:7],gamma=1.70,alternative="less")
submax documentation built on June 9, 2025, 5:10 p.m.
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| submax-package | R Documentation |
Effect Modification in Observational Studies Using the Submax Method
Description
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates. The submax or subgroup-maximum method of Lee et al. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Details
The DESCRIPTION file:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
The main function is submax(). Also helpful is score(). See their documentation.
Author(s)
Paul R. Rosenbaum [aut, cre]
Maintainer: Paul R. Rosenbaum <rosenbaum@wharton.upenn.edu>
References
Lee, K., Small, D. S., & Rosenbaum, P. R. (2018) <doi:10.1111/biom.12884> A powerful approach to the study of moderate effect modification in observational studies. Biometrics, 74(4), 1161-1170.
Examples
#Reproduces parts of Table 2 of Lee et al. (2018)
data(Active)
submax(Active$delta,Active[,1:7],gamma=1.70,alternative="less")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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