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 package implements two cure formulations on the Exponentiated Danish kernel:
pi(Z) modelled by logistic regression
on the incidence covariates, and a cured fraction
1 - pi(Z).theta(Z) = exp(Z' gamma); the cure fraction is
exp(-theta(Z)).Both are fitted via fit_bd_cure().
Both fits below use simulated data with a known cure structure.
library(BetaDanish)
set.seed(2026)
n <- 250
z <- stats::rbinom(n, 1, 0.5)
pi_susc <- stats::plogis(0.3 + 0.7 * z)
cured <- stats::rbinom(n, 1, 1 - pi_susc) == 1
T_true <- ifelse(cured, Inf,
rbetadanish(n, a = 1, b = 2, c = 1.5, k = 0.4))
C <- stats::rexp(n, 0.04)
time <- pmin(T_true, C)
status <- ifelse(T_true <= C, 1, 0)
dat <- data.frame(time = time, status = status, z = z)
cat("Sample size:", n, " Censoring rate:", round(mean(status == 0), 2), "\n")
#> Sample size: 250 Censoring rate: 0.45fit_mix <- fit_bd_cure(
formula_aft = survival::Surv(time, status) ~ 1,
formula_cure = ~ z,
data = dat,
type = "mixture",
n_starts = 3
)
summary(fit_mix)
#>
#> Call:
#> fit_bd_cure(formula_aft = survival::Surv(time, status) ~ 1, formula_cure = ~z,
#> data = dat, type = "mixture", n_starts = 3)
#>
#> Beta-Danish Cure Model (mixture)
#>
#> Estimate Std. Error z value Pr(>|z|)
#> log_b 1.26173 0.52210 2.4166 0.01566 *
#> log_c 0.43147 0.19961 2.1616 0.03065 *
#> delta_(Intercept) -1.39644 0.75934 -1.8390 0.06591 .
#> gamma_(Intercept) 0.22464 0.19373 1.1596 0.24623
#> gamma_z 0.56245 0.29138 1.9303 0.05357 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-Likelihood: -416.2507fit_prom <- fit_bd_cure(
formula_aft = survival::Surv(time, status) ~ 1,
formula_cure = ~ z,
data = dat,
type = "promotion",
n_starts = 3
)
summary(fit_prom)
#>
#> Call:
#> fit_bd_cure(formula_aft = survival::Surv(time, status) ~ 1, formula_cure = ~z,
#> data = dat, type = "promotion", n_starts = 3)
#>
#> Beta-Danish Cure Model (promotion)
#>
#> Estimate Std. Error z value Pr(>|z|)
#> log_b 1.56319 0.77860 2.0077 0.04468 *
#> log_c 0.39322 0.16852 2.3333 0.01963 *
#> delta_(Intercept) -2.03331 0.93058 -2.1850 0.02889 *
#> gamma_(Intercept) -0.19425 0.12818 -1.5155 0.12965
#> gamma_z 0.33526 0.17055 1.9657 0.04933 *
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> ---
#> Log-Likelihood: -416.1921?fit_bd_cure for full documentation?bd_bootstrap_ci for bootstrap confidence
intervals?plot.bd_cure for Cox-Snell residual plotsThese 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.