create_calibration_curve_times()

Create a time-dependent calibration curve across fixed horizons.

Usage

Source

create_calibration_curve_times(
    probs,
    reals,
    times,
    fixed_time_horizons,
    heuristics_sets,
    calibration_type="discrete",
    smooth_method="local_aj",
    bandwidth=None,
    size=600,
    color_values=["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#07004D", "#E6AB02", "#FE5F55", "#54494B", "#006E90", "#BC96E6", "#52050A", "#1F271B", "#BE7C4D", "#63768D", "#08A045", "#320A28", "#82FF9E", "#2176FF", "#D1603D", "#585123"],
    *,
    n_bins=10
)

This function generates time-dependent calibration curves evaluating predicted probabilities against observed outcomes over specified prediction horizons.

Parameters

probs: Dict[str, np.ndarray]

A dictionary mapping model or dataset names to 1-D numpy arrays of predicted probabilities.

reals: Union[np.ndarray, Dict[str, np.ndarray]]

True outcome indicators (0 for censored, 1 for event of interest, 2 for competing risk).

times: Union[np.ndarray, Dict[str, np.ndarray]]

Follow-up times corresponding to reals.

fixed_time_horizons: List[float]

List of prediction horizons (times) at which to evaluate calibration.

heuristics_sets: List[Dict[str, str]]

List of heuristic dictionaries defining censoring and competing risk adjustments.

calibration_type: str = "discrete"

Type of calibration plot, either "discrete" (binned) or "smooth". Defaults to "discrete".

smooth_method: str = "local_aj"

Smoothing method when calibration_type="smooth". Supported options are "local_aj" (Gerds’ local Aalen-Johansen/KM neighborhood estimation), "secondary_cox" (Austin, Harrell & McLernon secondary Cox regression with 3-knot restricted cubic splines on complementary log-log predictions), or "pseudo_values" (jackknife pseudo-values lowess). Defaults to "local_aj".

bandwidth: Union[float, None] = None

Bandwidth fraction for "local_aj" neighborhood smoothing. Defaults to None.

size: int = 600

Width and height of the Plotly figure in pixels. Defaults to 600.

color_values: List[str] = [
    "#1b9e77",
    "#d95f02",
    "#7570b3",
    "#e7298a",
    "#07004D",
    "#E6AB02",
    "#FE5F55",
    "#54494B",
    "#006E90",
    "#BC96E6",
    "#52050A",
    "#1F271B",
    "#BE7C4D",
    "#63768D",
    "#08A045",
    "#320A28",
    "#82FF9E",
    "#2176FF",
    "#D1603D",
    "#585123",
]


List of hex color strings for traces.

n_bins: int = 10

Number of bins for discrete calibration curves. Defaults to 10.

Returns

Figure

A Plotly Figure object representing the time-dependent calibration curve.

Raises

ValueError

If a heuristic set requests competing_heuristic='adjusted_as_censored'.