The goal of blendR is to blend two survival curves together from one to the other according to some defined blending function. Originally developed for the following paper
Che Z, Green N, Baio G. Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment. Med Decis Mak. 2022;43(3):299–310.
You can install the development version of blendR from GitHub with:
# install.packages("devtools")
devtools::install_github("StatisticsHealthEconomics/blendR")This is a basic example which shows you how to solve a common
problem. Using the TA174_FCR data set contained in the
blendR package, we fit exponential distribution survival
models with no covariates with the fit.models() function
from the survHE package. This employs the HMC sampler from
Stan behind the scenes. The external or long-term data
are obtained from an heuristic approach to simulating data consistent
with user-defined constraints. The results are then blended into a
single survival curve using the blendsurv() function.
library(blendR)
library(survHE)
## trial data
data("TA174_FCR", package = "blendR")
## externally estimated data
data_sim <- ext_surv_sim(t_info = 144,
S_info = 0.05,
T_max = 180)
obs_Surv <- fit.models(formula = Surv(death_t, death) ~ 1,
data = dat_FCR,
distr = "exponential",
method = "hmc")
ext_Surv <- fit.models(formula = Surv(time, event) ~ 1,
data = data_sim,
distr = "exponential",
method = "hmc")
blend_interv <- list(min = 48, max = 150)
beta_params <- list(alpha = 3, beta = 3)
ble_Surv <- blendsurv(obs_Surv, ext_Surv, blend_interv, beta_params)
plot(ble_Surv)https://statisticshealtheconomics.github.io/blendR/
Please submit contributions through Pull Requests,
following the contributing
guidelines.
To report issues and/or seek support, please file a new ticket in the issue tracker.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.