| Title: | Compare C-Statistics (Concordance) Between Survival Models |
| Version: | 0.1.0 |
| Description: | Compare C-statistics (concordance statistics) between two survival models, using either bootstrap resampling (Harrell's C) or Uno's C with perturbation-resampling (from the survC1 package). Returns confidence intervals and a p-value for the difference in C-statistics. Useful for evaluating and comparing predictive performance of survival models. Methods implemented for Uno's C are described in Uno et al. (2011) <doi:10.1002/sim.4154>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Depends: | R (≥ 3.5.0) |
| Imports: | boot |
| Suggests: | survival, survC1 |
| RoxygenNote: | 7.3.2 |
| URL: | https://github.com/Lemonade0924/compareCstat |
| BugReports: | https://github.com/Lemonade0924/compareCstat/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-06-05 04:41:56 UTC; Ning |
| Author: | Hairong Liu [aut], Ning Meng [aut, cre] |
| Maintainer: | Ning Meng <nmeng2@jh.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-06-06 13:30:01 UTC |
Compare C-statistics Between Two Models with Bootstrapped or Uno's C Confidence Intervals
Description
This function compares the C-statistics of two fitted models using either bootstrap resampling (Harrell's C) or Uno's C via perturbation-resampling (survC1 package).
Usage
compare_c_stat(
model_raw,
model_ext,
data,
R = 10,
ci_type = "perc",
method = "Harrell",
tau = NULL
)
Arguments
model_raw |
A fitted model (e.g., coxph) representing the base model. |
model_ext |
A fitted model (e.g., coxph) representing the extended model. |
data |
The dataset used for fitting the models. |
R |
Number of bootstrap or perturbation-resampling replications. Default is 100. |
ci_type |
Type of confidence interval to return ("perc", "norm", "basic", etc., for Harrell's C). |
method |
Which C-statistic to use: "Harrell" (default) or "Uno". |
tau |
Truncation time for Uno's C (default is max observed time in your data). |
Value
A data frame showing C-statistics for each model, their confidence intervals, and the p-value for the difference.
References
Uno H, Cai T, Pencina MJ, D'Agostino RB, Wei LJ. (2011) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine, 30(10):1105-1117. doi:10.1002/sim.4154
Examples
library(survival)
data(lung)
lung$status <- ifelse(lung$status == 2, 1, 0)
model1 <- coxph(Surv(time, status) ~ age, data = lung)
model2 <- coxph(Surv(time, status) ~ age + sex, data = lung)
compare_c_stat(model1, model2, data = lung, R = 10, method = "Harrell")
compare_c_stat(model1, model2, data = lung, R = 10, method = "Uno")
compare_c_stat(model1, model2, data = lung, R = 10, method = "Uno", tau = 365.25*2)