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Choosing the RMST horizon

The restriction horizon is part of the prediction question. It should be chosen before fitting when a clinically meaningful decision window is available.

Preferred clinical workflow

Choose τ from the endpoint, intended decision and observed follow-up support. Document the choice before evaluating model performance.

model = SurvFMRMSTRegressor(backbone="tabpfn", tau=365.0)

Generic benchmark workflow

When no external horizon is available, the package can derive a quantile from observed event times in the training fold only:

model = SurvFMRMSTRegressor(
    backbone="tabpfn",
    tau=None,
    tau_quantile=0.8,
    min_events_for_tau=5,
)
model.fit(X_train, time_train, event_train)
print(model.horizon_selection_.to_dict())

This avoids test-outcome leakage but makes the estimand split-specific. It is a benchmark convenience, not a substitute for a prespecified clinical horizon.