Royston-Sauerbei D statistic

Compute the Royston-Sauerbei D statistic

Usage

royston_survival(data, ...)

# S3 method for class 'data.frame'
royston_survival(data, truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

royston_survival_vec(truth, estimate, na_rm = TRUE, case_weights = NULL, ...)

Arguments

data: A data.frame containing the columns specified by truth and ....
...: Not currently used.
truth: The column identifier for the true survival result (that is created using survival::Surv().). This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, an survival::Surv() object.
estimate: The column identifier for the predicted linear predictor, this should be a numeric variable. This should be an unquoted column name although this argument is passed by expression and supports quasiquotation (you can unquote column names). For _vec() functions, a numeric vector.
na_rm: A logical value indicating whether NA values should be stripped before the computation proceeds.
case_weights: The optional column identifier for case weights. This should be an unquoted column name that evaluates to a numeric column in data. For _vec() functions, a numeric vector, hardhat::importance_weights(), or hardhat::frequency_weights().

Value

A tibble with columns .metric, .estimator, and .estimate and 1 row of values.

For grouped data frames, the number of rows returned will be the same as the number of groups.

For royston_survival_vec(), a single numeric value (or NA).

Details

Royston D statistic is a metric that should be maximized. The output ranges from 0 to 1, with 1 indicating perfect prognostic separation.

Royston and Sauerbrei proposed $R^2_D$ as a measure of explained variation on the log relative hazard scale based on the authors’ D statistic. D measures prognostic separation of survival curves, and is closely related to the standard deviation of the prognostic index.

Larger values of the score are associated with better model performance.

References

Royston, P., Sauerbrei, W., "A new measure of prognostic separation in survival data", Statistics in Medicine, 23, 723-748, 2004.

Author

Hannah Frick

Examples

royston_survival(
  data = lung_surv,
  truth = surv_obj,
  estimate = .pred_linear_pred
)
#> # A tibble: 1 × 3
#>   .metric          .estimator .estimate
#>   <chr>            <chr>          <dbl>
#> 1 royston_survival standard       0.116