Kernel-weighted Cox regression for exploring treatment effect heterogeneity and candidate predictive biomarkers.
# install.packages("devtools")
devtools::install_github("richJJackson/chestR")Or install from a local checkout:
devtools::install("path/to/chestR")library(survival)
library(chestR)
# Fit a global Cox model
base <- coxph(Surv(time, status) ~ treatment + covariate, data = mydata)
# Local estimates over a biomarker grid
cr <- chestr(base, mydata[, c("biom1", "biom2")], grid.size = 25,
treat_term = "treatment")
# Visualise local treatment effect
plot(cr, trt.param = "treatment")
# Optional permutation test of constant treatment effect
# tst <- chestr_test(cr, B = 99, seed = 1)See vignette("chestr-workflow", package = "chestR")
after install, or inst/examples/simulation.R for a longer
simulation script.
Open chestR.Rproj in RStudio, then:
devtools::load_all()
devtools::test()
devtools::document()
devtools::check()MIT
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