samTEMsel (R package version 0.1.0)

Sparse Additive models for Treatment Effect-Modifier Selection

An implementation of a constrained sparse additive regression for modeling interaction effects between a categorical treatment variable and a set of pretreatment covariates on a scalar-valued outcome; the regression simultaneousely conducts treatment effect-modifier variable selection. The method can effectively identify treatment effect-modifiers exhibiting possibly nonlinear interactions with the treatment. The selected pretreatment characteristics and the associated nonzero component functions can be used as a new set of data-driven features for making individualized treatment recommendations in further analysis. We refer to Park, Petkova, Tarpey, and Ogden (2020) doi:10.1016/j.jspi.2019.05.008 and Park, Petkova, Tarpey, and Ogden (2020) "A constrained sparse additive model for treatment effect-modifier selection" (pre-print) for detail of the method. The wrapper function of this package is cv.samTEMsel().

Description

• samTEMsel - samTEMsel main function
• cv.samTEMsel - samTEMsel cross-validation function for tuning parameter selection
• predict_samTEMsel - samTEMsel prediction function
• make_ITR - make individualized treatment recommendations (ITRs) based on a samTEMsel object
• plot_samTEMsel - plot component functions from a samTEMsel object

To run:

To install an R package, start by installing the "devtools" package (from CRAN). On R, type:

install.packages("devtools")  # install the devtools package from CRAN
library(devtools)

To install the "samTEMsel" package from github, type:

devtools::install_github("syhyunpark/samTEMsel")  # install the samTEMsel package from github 
library(samTEMsel)   # load the samTEMsel package to R 

To see some of the example codes appearing in the "help" menu, type:

?samTEMsel   
?cv.samTEMsel