concordance_mcm: C-statistic for mixture cure models

In hdcuremodels: High-Dimensional Cure Models

C-statistic for mixture cure models

Description

This function calculates the C-statistic using the cure status weighting (CSW) method proposed by Asano and Hirakawa (2017).

Usage

concordance_mcm(object, newdata, cure_cutoff = 5, model_select = "AIC")

Arguments

object	a mixturecure object resulting from curegmifs, cureem, cv_curegmifs, or cv_cureem.
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 representing 5 years). Users should be careful to note the time scale of their data and adjust this according to the time scale and clinical application.
model_select	either a case-sensitive parameter for models fit using curegmifs or cureem or any numeric step along the solution path can be selected. The default is model_select = "AIC" which calculates the predicted values using the coefficients from the model achieving the minimum AIC. The complete list of options are: "AIC" for the minimum AIC (default). "mAIC" for the minimum modified AIC. "cAIC" for the minimum corrected AIC. "BIC", for the minimum BIC. "mBIC" for the minimum modified BIC. "EBIC" for the minimum extended BIC. "logLik" for the step that maximizes the log-likelihood. model_select = n where n is any numeric value from the solution path. This option has no effect for objects fit using cv_curegmifs or cv_cureem.

Value

value of C-statistic for the cure models.

References

Asano, J. and Hirakawa, H. (2017) Assessing the prediction accuracy of a cure model for censored survival data with long-term survivors: Application to breast cancer data. Journal of Biopharmaceutical Statistics, 27:6, 918–932.

See Also

auc_mcm

Examples

library(survival)
withr::local_seed(1234)
temp <- generate_cure_data(n = 100, j = 10, n_true = 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
)
concordance_mcm(fit, model_select = "cAIC")
concordance_mcm(fit, newdata = testing, model_select = "cAIC")

hdcuremodels documentation built on Aug. 8, 2025, 7:38 p.m.