AUC {hdcuremodels} | R Documentation |
AUC for cure prediction using mean score imputation
Description
This function calculates the AUC for cure prediction using the mean score imputation (MSI) method proposed by Asano et al.
Usage
AUC(object, newdata, cure_cutoff = 5, model.select = "AIC")
Arguments
object |
a |
newdata |
an optional data.frame that minimally includes the incidence and/or latency variables to use for predicting the response. If omitted, the training data are used. |
cure_cutoff |
cutoff value for cure, used to produce a proxy for the unobserved cure status; default is 5. |
model.select |
for models fit using |
Value
Returns the AUC value for cure prediction using the mean score imputation (MSI) method.
References
Asano, J., Hirakawa, H., Hamada, C. (2014) Assessing the prediction accuracy of cure in the Cox proportional hazards cure model: an application to breast cancer data. Pharmaceutical Statistics, 13:357–363.
See Also
Examples
library(survival)
set.seed(1234)
temp <- generate_cure_data(N = 100, J = 10, nTrue = 10, A = 1.8)
training <- temp$Training
testing <- temp$Testing
fit <- curegmifs(Surv(Time, Censor) ~ .,
data = training, x.latency = training,
model = "weibull", thresh = 1e-4, maxit = 2000,
epsilon = 0.01, verbose = FALSE)
AUC(fit)
AUC(fit, newdata = testing)