The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
The R package ‘survivalSL’ contains a variety of functions to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities. Several learners are proposed: proportional hazard (PH) regressions, penalized PH semi-parametric models, accelerated failure times (AFT) models, neural networks, random survival forests, etc.). We proposed also a variety of loss functions for the estimation of the weights (concordance index, Brier score, area under the time-dependent ROC curve, negative binomial log-likelihood, etc.). S3 methods are included to evaluate the predictive capacities, as well as predicting survival curves from new observations.
# Simulate a training and validation samples
n.valid <- 500 # sample size for validation
n.learn <- 200 # sample size for training
n <- n.valid + n.learn # overall sample size
max.time <- 50 # maximum follow-up time
mean.x <- 0; sd.x <- 1 # normal distribution of the quantitative predictors
proba.x <- .5 # proportion of the binary predictors
a <- 2; b <- .05 # Weibull baseline distribution of the PH model
beta <- c(log(1.8), log(1.8), log(1.3), 0, 0, 0) # regression coefficients
# simulation of the training and validation samples
x1 <- rnorm(n, mean.x, sd.x)
x2 <- rbinom(n, 1, proba.x)
x3 <- rbinom(n, 1, proba.x)
x4 <- rnorm(n, mean.x, sd.x)
x5 <- rbinom(n, 1, proba.x)
x6 <- rbinom(n, 1, proba.x)
x <- cbind(x1, x2, x3, x4, x5, x6) # matrix of the potential predictors
times <- 1/b*((-exp(-1*(x %*% beta))*(log(1-runif(n, 0, 1))))**(1/a)) # time to event
censoring <- runif(n, min=0, max=max.time)
status <- ifelse(times <= censoring, 1, 0) # event status
obs.times <- ifelse(times <= censoring, times, censoring) # follow-up times
data <- cbind(obs.times, status, as.data.frame(x))
data.simul <- list(data[1:n.valid,], data[(n.valid+1):n,])
# model estimation with default parameters and three learners
slres <- survivalSL(
methods=c("LIB_COXen", "LIB_AFTgamma", "LIB_PHexponential"),
metric="ci", data=data.simul[[1]], times="obs.times",
failures="status", cov.quanti=c("x1","x4"),
cov.quali=c("x2","x3","x5","x6"), progress = FALSE)
#> Warning in min(group %in% colnames(data)): no non-missing arguments to min;
#> returning Inf
# prognostic capacities from training sample
summary(slres, digits=3)
#> ci auc bs ibs ribs bll ibll ribll
#> 1 0.671 0.723 0.197 0.094 0.092 0.581 0.303 0.306
# prognostic capacities from validation sample
summary(slres, newdata=data.simul[[2]], digits=3)
#> ci auc bs ibs ribs bll ibll ribll
#> 1 0.677 0.75 0.187 0.092 0.09 0.557 0.297 0.299To install the latest release from CRAN:
install.packages("survivalSL")To install the development version from GitHub:
remotes::install_github("foucher-y/survivalSL")You can report any issues at this link.
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.
Health stats visible at Monitor.