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C-statistic for mixture cure models

Source: R/concordance_mcm.R
concordance_mcm.Rd

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")
#> [1] 0.7919723
concordance_mcm(fit, newdata = testing, model_select = "cAIC")
#> [1] 0.5584605