prepare_performance_data_times()

Prepare performance data for models with time-to-event outcomes.

Usage

Source

prepare_performance_data_times(
    probs,
    reals,
    times,
    fixed_time_horizons,
    heuristics_sets=[{"censoring_heuristic": "adjusted", "competing_heuristic": "adjusted_as_negative"}],
    stratified_by=("probability_threshold",),
    by=0.01
)

This function calculates a comprehensive set of performance metrics for models predicting time-to-event outcomes. It handles censored data and competing events by applying specified heuristics at different time horizons. The function first bins the data using prepare_binned_classification_data_times and then computes cumulative, Aalen-Johansen-based performance metrics.

The resulting dataframe is the primary input for time-dependent plotting functions.

Parameters

probs: Dict[str, np.ndarray]

A dictionary mapping model or dataset names (str) to their predicted probabilities of an event occurring by a given time.

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

The true event statuses. Can be a single array or a dictionary. Labels should be integers indicating the outcome (e.g., 0=censored, 1=event of interest, 2=competing event).

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

The event or censoring times corresponding to the reals. Can be a single array or a dictionary.

fixed_time_horizons: list[float]

A list of numeric time points at which to evaluate the model’s performance. Integer inputs are accepted and normalized to floats.

heuristics_sets: list[Dict] = [{"censoring_heuristic": "adjusted", "competing_heuristic": "adjusted_as_negative"}]


A list of dictionaries, each specifying how to handle censored data and competing events. The default is [{"censoring_heuristic": "adjusted", "competing_heuristic": "adjusted_as_negative"}].

stratified_by: Sequence[str] = ("probability_threshold",)


Variables by which to stratify the analysis. Defaults to ("probability_threshold",).

by: float = 0.01

The step size for probability thresholds. Defaults to 0.01.

Returns

pl.DataFrame

A Polars DataFrame with performance metrics computed across probability thresholds and time horizons. It includes columns for cutoffs, time points, heuristics, and performance measures.