survex (version 1.2.0)

model_performance: Dataset Level Performance Measures

Description

This function calculates metrics for survival models. The metrics calculated are C/D AUC, Brier score, and their integrated versions, as well as concordance index. It also can calculate ROC curves for specific selected time points.

Usage

model_performance(explainer, ...)
# S3 method for surv_explainer
model_performance(
  explainer,
  ...,
  type = "metrics",
  metrics = c(`C-index` = c_index, `Integrated C/D AUC` = integrated_cd_auc,
    `Brier score` = brier_score, `Integrated Brier score` = integrated_brier_score,
    `C/D AUC` = cd_auc),
  times = NULL
)

Value

An object of class "model_performance_survival". It's a list of metric values calculated for the model. It contains:

• Harrell's concordance index [1]

• Brier score [2, 3]

• C/D AUC using the estimator proposed by Uno et. al [4]

• integral of the Brier score

• integral of the C/D AUC

Arguments

explainer

an explainer object - model preprocessed by the explain() function

...

other parameters, currently ignored

type

character, either "metrics" or "roc". If "metrics" then performance metrics are calculated, if "roc" ROC curves for selected time points are calculated.

metrics

a named vector containing the metrics to be calculated. The values should be standardized loss functions. The functions can be supplied manually but has to have these named parameters (y_true, risk, surv, times), where y_true represents the survival::Surv object with observed times and statuses, risk is the risk score calculated by the model, and surv is the survival function for each observation evaluated at times.

times

a numeric vector of times. If type == "metrics" then the survival function is evaluated at these times, if type == "roc" then the ROC curves are calculated at these times.

References

• [1] Harrell, F.E., Jr., et al. "Regression modelling strategies for improved prognostic prediction." Statistics in Medicine 3.2 (1984): 143-152.

• [2] Brier, Glenn W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review 78.1 (1950): 1-3.

• [3] Graf, Erika, et al. "Assessment and comparison of prognostic classification schemes for survival data." Statistics in Medicine 18.17‐18 (1999): 2529-2545.

• [4] Uno, Hajime, et al. "Evaluating prediction rules for t-year survivors with censored regression models." Journal of the American Statistical Association 102.478 (2007): 527-537.

Examples

# \donttest{
library(survival)
library(survex)


cph <- coxph(Surv(time, status) ~ ., data = veteran, model = TRUE, x = TRUE, y = TRUE)
rsf_ranger <- ranger::ranger(Surv(time, status) ~ .,
    data = veteran,
    respect.unordered.factors = TRUE,
    num.trees = 100,
    mtry = 3,
    max.depth = 5
)

rsf_src <- randomForestSRC::rfsrc(Surv(time, status) ~ .,
    data = veteran
)

cph_exp <- explain(cph)
rsf_ranger_exp <- explain(rsf_ranger,
    data = veteran[, -c(3, 4)],
    y = Surv(veteran$time, veteran$status)
)
rsf_src_exp <- explain(rsf_src)


cph_model_performance <- model_performance(cph_exp)
rsf_ranger_model_performance <- model_performance(rsf_ranger_exp)
rsf_src_model_performance <- model_performance(rsf_src_exp)

print(cph_model_performance)

plot(rsf_ranger_model_performance, cph_model_performance,
    rsf_src_model_performance,
    metrics_type = "scalar"
)

plot(rsf_ranger_model_performance, cph_model_performance, rsf_src_model_performance)

cph_model_performance_roc <- model_performance(cph_exp, type = "roc", times = c(100, 250, 500))
plot(cph_model_performance_roc)
# }