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.
Installation
You can install the development version of blendR from GitHub with:
# install.packages("devtools")
devtools::install_github("StatisticsHealthEconomics/blendR")Example
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)
Contributing
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.