bootstrap()

Bootstrap confidence interval for a Kaplan-Meier summary statistic.

Usage

Source

bootstrap(
    surv,
    statistic,
    *,
    by=None,
    weights=None,
    n_boot=1000,
    conf_level=0.95,
    ci_type="percentile",
    seed=None,
    tau=None,
    p=None,
    times=None,
)

Resamples subjects with replacement, fits a Kaplan-Meier estimator on each resample, and extracts the statistic of interest. The bootstrap distribution is then used to construct a confidence interval.

Parameters

surv: Surv

A Surv response (right-censored or counting-process).

statistic: str | Callable[[KaplanMeier], float]

The quantity to bootstrap. Pass a string for built-in statistics:

  • "median": median survival time.
  • "rmst": restricted mean survival time (requires tau=).
  • "quantile": survival quantile (requires p=).
  • "survival": survival probability at a fixed time (requires times=).
  • "median_diff": difference in median survival between two groups (requires by=).
  • "rmst_diff": difference in RMST between two groups (requires by= and tau=).
  • "survival_diff": difference in survival probability at a fixed time between two groups (requires by= and times=).

Alternatively, pass a callable that takes a fitted KaplanMeier and returns a float.

by: Any = None

Grouping variable for two-sample comparisons. Required for "_diff" statistics. Must define exactly two groups.

weights: Any = None

Optional case weights passed through to each bootstrap KM fit.

n_boot: int = 1000

Number of bootstrap replicates (default 1000).

conf_level: float = 0.95

Confidence level (default 0.95).

ci_type: str = "percentile"

Confidence interval type: "percentile" (default), "normal", or "bca" (bias-corrected and accelerated).

seed: int | None = None

Random seed for reproducibility.

tau: float | None = None

Upper time limit for RMST statistics.

p: float | None = None

Quantile level for "quantile" statistic.

times: float | None = None
Time point for "survival" and "survival_diff" statistics.

Returns

BootstrapResult
Point estimate, standard error, confidence interval, and the full bootstrap distribution.