Description
Treatment Switching.
Description
Implements rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials.
README.md
trtswitch
trtswitch provides methods for treatment switching adjustment in randomized clinical trials, including:
- rank preserving structural failure time model (RPSFTM)
- iterative parameter estimation (IPE)
- inverse probability of censoring weights (IPCW)
- marginal structural model (MSM)
- simple two-stage estimation (TSEsimp)
- improved two-stage estimation with g-estimation (TSEgest)
Installation
Install from GitHub:
# install.packages("remotes")
remotes::install_github("kaifenglu/trtswitch")
Example: RPSFTM
library(trtswitch)
library(dplyr)
# Build treatment exposure proportion used by rpsftm
# in the one-way switching example dataset.
data <- immdef %>%
mutate(rx = 1 - xoyrs / progyrs)
fit <- rpsftm(
data = data,
id = "id",
time = "progyrs",
event = "prog",
treat = "imm",
rx = "rx",
censor_time = "censyrs",
boot = FALSE
)
fit
# Key estimates
fit$psi
fit$hr
fit$psi_CI
fit$hr_CI
Documentation
- Website: https://kaifenglu.github.io/trtswitch/
- Function reference: https://kaifenglu.github.io/trtswitch/reference/
- Issues: https://github.com/kaifenglu/trtswitch/issues.