Tidy time-varying AUC from a Random Hazard Forest

Extracts the time-dependent AUC curve from randomForestRHF::auct.rhf() into a tidy long data frame, one row per time point, with bootstrap confidence bounds when available and the integrated AUC (iAUC) summary attached as an attribute.

Usage

gg_auct(object, ...)

# S3 method for class 'auct.rhf'
gg_auct(object, ...)

# S3 method for class 'rhf'
gg_auct(
  object,
  marker = c("chf", "haz"),
  auct_fit = NULL,
  method = c("cumulative", "incident"),
  ...
)

Arguments

object: A fitted rhf object from randomForestRHF.
...: Further arguments passed to randomForestRHF::auct.rhf(), for example bootstrap.rep to request confidence bounds, or riskset for the incident definition. Ignored when auct_fit is supplied.
marker: Risk marker for the AUC: "chf" (cumulative hazard, default) or "haz" (hazard). Not used when auct_fit is supplied, though the value is still validated.
auct_fit: Optional precomputed randomForestRHF::auct.rhf() result (class "auct.rhf") for the same object. NULL (default) computes it. Supply it to reuse an expensive bootstrap run.
method: Which time-dependent AUC definition to compute, passed to randomForestRHF::auct.rhf(). "cumulative" (default) ranks accumulated risk through a horizon; "incident" ranks local failures within the risk set at each time. See the note below on choosing between them. Not used when auct_fit is supplied, though the value is still validated.

Value

A data.frame of class c("gg_auct", "data.frame") with columns time, auc, se, lower, upper, marker (CI columns NA when no bootstrap), an iauc attribute (a list with uno, std, uno.se, std.se, conf.level), a method attribute ("cumulative" or "incident", taken from the auct.rhf fit, which is what plot.gg_auct() names on its y axis), and a provenance attribute derived from object (source, family, ntree, n).

Note

The two definitions answer different questions rather than better and worse versions of the same one. Cumulative/dynamic AUC ranks accumulated risk through a horizon, comparing subjects who have failed by that horizon against subjects still event-free at it. Incident/dynamic AUC ranks local failures within the risk set at each time. Pick the one that matches the question you are asking, and read the two curves as separate estimands rather than as a check on each other.

References

Ishwaran H, Hsich EM, Kogalur UB, Lee DKK (2026). Random Hazard Forests. arXiv:2608.21597. doi:10.48550/arXiv.2608.21597 .

Ishwaran H, Kogalur UB (2026). randomForestRHF: Random Hazard Forests. R package version 2.0.3. https://CRAN.R-project.org/package=randomForestRHF.

Examples

# \donttest{
if (requireNamespace("randomForestRHF", quietly = TRUE)) {
  data(pbc, package = "randomForestSRC")
  d <- randomForestRHF::convert.counting(
    survival::Surv(days, status) ~ ., na.omit(pbc))
  o <- randomForestRHF::rhf("Surv(id, start, stop, event) ~ .", d, ntree = 30)
  plot(gg_auct(o, marker = "chf"))
}

# }