create_roc_curve_times()

Creates a time-dependent Receiver Operating Characteristic (ROC) curve.

Usage

create_roc_curve_times(
    probs,
    reals,
    times,
    fixed_time_horizons,
    heuristics_sets=[{"censoring_heuristic": "adjusted", "competing_heuristic": "adjusted_as_negative"}],
    by=0.01,
    stratified_by=["probability_threshold"],
    size=600,
    color_values=["#1b9e77", "#d95f02", "#7570b3", "#e7298a", "#07004D", "#E6AB02", "#FE5F55", "#54494B", "#006E90", "#BC96E6", "#52050A", "#1F271B", "#BE7C4D", "#63768D", "#08A045", "#320A28", "#82FF9E", "#2176FF", "#D1603D", "#585123"]
)

This function generates an ROC curve for time-to-event models. It evaluates the model’s performance at specified time horizons, handling censored data and competing risks according to the chosen heuristics.

Parameters

probs: Dict[str, np.ndarray]: A dictionary of predicted probabilities.
reals: Union[np.ndarray, Dict[str, np.ndarray]]: The true event statuses (e.g., 0=censored, 1=event, 2=competing).
times: Union[np.ndarray, Dict[str, np.ndarray]]: The event or censoring times.
fixed_time_horizons: list[float]: A list of time points for performance evaluation.
heuristics_sets: list[Dict] = [{"censoring_heuristic": "adjusted", "competing_heuristic": "adjusted_as_negative"}]: Specifies how to handle censored data and competing events.
by: float = 0.01: The step size for probability thresholds. Defaults to 0.01.
stratified_by: Sequence[str] = ["probability_threshold"]: Variables for stratification. Defaults to ["probability_threshold"].
size: int = 600: The width and height of the plot 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", ]: A list of hex color strings for the plot lines.

Returns

Figure: A Plotly Figure object representing the time-dependent ROC curve.