prepare_performance_data_times

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,
)

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

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

Name	Type	Description	Default
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.	required
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).	required
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.	required
fixed_time_horizons	list[float]	A list of time points at which to evaluate the model’s performance.	required
heuristics_sets	list[Dict]	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"}].	[{'censoring_heuristic': 'adjusted', 'competing_heuristic': 'adjusted_as_negative'}]
stratified_by	Sequence[str]	Variables by which to stratify the analysis. Defaults to ("probability_threshold",).	('probability_threshold',)
by	float	The step size for probability thresholds. Defaults to 0.01.	0.01

Returns

Name	Type	Description
	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.