jemus42 / coxboost

R package CoxBoost

CoxBoost

CoxBoost fits Cox proportional hazards models by componentwise likelihood based boosting. It is especially suited for models with a large number of predictors and allows for mandatory covariates with unpenalized parameter estimates.

Installation

You can install the development version of CoxBoost from GitHub with:

# install.packages("pak")
pak::pak("binderh/CoxBoost")

Example

A new feature that currently is only available in the version here, but not on CRAN, is weighted stratified regression for dealing with known subgroups. In particular, the new function resample.CoxBoost (and associated plotting routines stabtrajec and weightfreqmap) allows to explore different weightings of subgroups. The plotting routines help to understand which covariates might be relevant just for one subgroup, or relevant to some degree for more subgroups. The following is a usage example, adapted from the package help page of resample.CoxBoost:

library(CoxBoost)
n <- 400
p <- 1000
set.seed(129)
group <- rbinom(n, 1, 0.5)
x <- matrix(rnorm(n * p, 0, 1), n, p)
beta.vec1 <- c(c(1, 1, 1, 1, 1), rep(0, p - 5))
beta.vec0 <- c(c(0, 0, 0, 0, 0), rep(0, p - 5))
linpred <- ifelse(group == 1, x %*% beta.vec1, x %*% beta.vec0)
set.seed(1234)
real.time <- (-(log(runif(n))) / (1 / 20 * exp(linpred)))
cens.time <- rexp(n, rate = 1 / 20)
obs.status <- ifelse(real.time <= cens.time, 1, 0)
obs.time <- ifelse(real.time <= cens.time, real.time, cens.time)

RIF <- resample.CoxBoost(
  time = obs.time, status = obs.status, x = x, rep = 100, maxstepno = 200, multicore = FALSE,
  mix.list = c(0.001, 0.01, 0.05, 0.1, 0.25, 0.35, 0.5, 0.7, 0.9, 0.99),
  stratum = group, stratnotinfocus = 0, penalty = sum(obs.status) * (1 / 0.02 - 1),
  criterion = "hscore", unpen.index = NULL
)

stabtrajec(RIF)
weightfreqmap(RIF)