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chestR

Kernel-weighted Cox regression for exploring treatment effect heterogeneity and candidate predictive biomarkers.

Installation

# install.packages("devtools")
devtools::install_github("richJJackson/chestR")

Or install from a local checkout:

devtools::install("path/to/chestR")

Quick start

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.

Development

Open chestR.Rproj in RStudio, then:

devtools::load_all()
devtools::test()
devtools::document()
devtools::check()

Related work

• Bonetti M, Gelber RD (2000). Statistics in Medicine.
• Liu Y, Lu W, Chen G (2015). Local partial-likelihood test. Statistics in Medicine.

License

MIT

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.