Concordance index for right-censored data — concordance

Compute the Concordance index for right-censored data

Usage

concordance_survival(data, ...)

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

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

Arguments

data: A data.frame containing the columns specified by truth and ....
...: Currently not 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 time, this should be a numeric variables. 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 concordance_survival_vec(), a single numeric value (or NA).

Details

The concordance index is defined as the proportion of all comparable pairs in which the predictions and outcomes are concordant.

Two observations are comparable if:

both of the observations experienced an event (at different times), or
the observation with the shorter observed survival time experienced an event, in which case the event-free subject “outlived” the other.

A pair is not comparable if they experienced events at the same time.

Concordance intuitively means that two samples were ordered correctly by the model. More specifically, two samples are concordant, if the one with a higher estimated risk score has a shorter actual survival time.

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

References

Harrell, F.E., Califf, R.M., Pryor, D.B., Lee, K.L., Rosati, R.A, “Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors”, Statistics in Medicine, 15(4), 361-87, 1996.

Author

Emil Hvitfeldt

Examples

concordance_survival(
  data = lung_surv,
  truth = surv_obj,
  estimate = .pred_time
)
#> # A tibble: 1 × 3
#>   .metric              .estimator .estimate
#>   <chr>                <chr>          <dbl>
#> 1 concordance_survival standard       0.637