pirate: Generated Effect Modifier

An implementation of the generated effect modifier (GEM) method. This method constructs composite variables by linearly combining pre-treatment scalar patient characteristics to create optimal treatment effect modifiers in linear models. The optimal linear combination is called a GEM. Treatment is assumed to have been assigned at random. For reference, see E Petkova, T Tarpey, Z Su, and RT Ogden. Generated effect modifiers (GEMs) in randomized clinical trials. Biostatistics (First published online: July 27, 2016, <doi:10.1093/biostatistics/kxw035>).

Author
Eva Petkova, Zhe Su
Date of publication
2016-11-08 00:30:07
Maintainer
Zhe Su <Zhe.Su@nyumc.org>
License
MIT + file LICENSE
Version
1.0.0

View on CRAN

Man pages

data_generator
Functions for Simulating Data
effectSize
Effect Size Calculation
gem_fit
GEM Fit
gem_test
Implement Fitted GEM criterior on a Data Set
permute_pvalue
Calculation of permutation p-value

Files in this package

pirate
pirate/src
pirate/src/Makevars
pirate/src/gemCpp.cpp
pirate/src/RcppExports.cpp
pirate/NAMESPACE
pirate/NEWS.md
pirate/R
pirate/R/gem_fit.R
pirate/R/gem_test.R
pirate/R/effect_size.R
pirate/R/RcppExports.R
pirate/R/permute_pvalue.R
pirate/R/data_generator.R
pirate/README.md
pirate/MD5
pirate/DESCRIPTION
pirate/man
pirate/man/gem_test.Rd
pirate/man/gem_fit.Rd
pirate/man/data_generator.Rd
pirate/man/permute_pvalue.Rd
pirate/man/effectSize.Rd
pirate/LICENSE