Description Usage Arguments Details Value Author(s) References See Also Examples
These functions calculate the survival analysis metric measured of a system compared to a hold-out test set. The measurement and the "truth" have a survival time and a censoring indicator 0/1 indicating if the event result or the event.
1 2 3 4 5 6 7 | tdbrier(data, mod, ...)
get_tdbrier(data, mod, ...)
integrate_tdbrier(x, ...)
integrate_tdbrier_reference(x, ...)
|
data |
For the default functions, a datframe containing survival (time), and status (0:censored/1:event), and the explanatory variables. |
mod |
Coxph model object fitted with coxph (survival). |
The Brier score is defined as the squared distance between the expected survival probability and the observed survival. Therefore, it measures the discrepancy between observation and model-based prediction.
The integrated Brier Score summarises the Brier Score over the range of observed events.Similar to the original Brier score [40] the iBrier: ranges from 0 to 1; the model with an out-of-training sample value closer to 0 outperforms the rest.
A tdBrier object
Carlos S Traynor
Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL http://www.jstatsoft.org/v50/i11/.
[iBrier]
1 2 3 4 5 6 7 8 9 10 | require(survival)
require(dplyr)
data(lung)
lung <- lung %>%
mutate(status = (status == 2))
mod <- coxph(Surv(time, status)~ age, data = lung)
tdbrier <- get_tdbrier(lung, mod)
integrate_tdbrier(tdroc)
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