Fits mixture and promotion-time (non-mixture) cure models using the Beta-Danish AFT baseline.
Usage
fit_bd_cure(
formula_aft,
formula_cure,
data,
type = c("mixture", "promotion"),
n_starts = 10,
method = "BFGS"
)Arguments
- formula_aft
A formula for the latency component (e.g., `Surv(time, status) ~ age`).
- formula_cure
A one-sided formula for the incidence/cure component (e.g., `~ treatment`).
- data
A data frame containing the variables.
- type
Character; either `"mixture"` or `"promotion"`.
- n_starts
Integer; number of random starts for optimization.
- method
Optimization method passed to `maxLik`.
Details
In the **mixture** model, the population is split into susceptible and cured fractions. The susceptible probability is modeled via logistic regression: `pi = exp(Z
In the **promotion-time** (non-mixture) model, the cure fraction is derived from a latent Poisson process of clonogenic cells: `theta = exp(Z The cure fraction is `exp(-theta)`.