curesurv (version 0.1.2)

AIC.curesurv: Akaike's An Information Criterion for cure models

Description

Calculates the Akaike's "An Information Criterion" for fitted models from curesurv

Usage

# S3 method for curesurv
AIC(object, ..., k = 2)

Value

the value corresponds to the AIC calculated from the log-likelihood of the fitted model if just one object is provided. If multiple objects are provided, a data.frame with columns corresponding to the objects and row representing the AIC

Arguments

object

a fitted model object obtained from curesurv

...

optionally more fitted model objects obtained from curesurv.

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

Details

When comparing models fitted by maximum likelihood to the same data, the smaller the AIC, the better the fit.

However in our case, one should be careful when comparing the AIC. Specifically, when one implements a mixture cure model with curesurv without correcting the rate table (pophaz.alpha=FALSE), one is not obligated to specify cumpophaz. However, you cannot compare a model where cumpophaz is not specified with a model where cumpophaz is specified. If one wants to compare different models using AIC, one should always specify cumpophaz when using the curesurv function.

Examples

# \donttest{

library("curesurv")
library("survival")

 testiscancer$age_crmin <- (testiscancer$age- min(testiscancer$age)) /
              sd(testiscancer$age)

fit_m1_ad_tneh <- curesurv(Surv(time_obs, event) ~ z_tau(age_crmin) +
                          z_alpha(age_crmin),
                          pophaz = "ehazard",
                          cumpophaz = "cumehazard",
                          model = "nmixture", dist = "tneh",
                          link_tau = "linear",
                          data = testiscancer,
                          method_opt = "L-BFGS-B")

 AIC(fit_m1_ad_tneh)

 # }