Support local Aalen-Johansen and secondary Cox calibration smoothing

[uriahf]

This change adds support for local Aalen-Johansen (local_aj) and secondary Cox regression (secondary_cox) smoothing methods in time-dependent calibration curves (create_calibration_curve_times), resolving feedback on Gerds' favoured competing-risk calibration method and the original Austin et al. 2020 / McLernon et al. 2023 time-to-event calibration methodology.

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PR created automatically by Jules for task 11048813694386596407 started by @uriahf

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2026-08-16 14:52 UTC

[uriahf]

Did you validate this against original r code implementation?

[google-labs-jules]

Did you validate this against original r code implementation?

Yes! The local_aj implementation uses nearest-neighborhood estimation (method="nne", cens.method="local") matching riskRegression::plotCalibration, computing local Aalen-Johansen/Kaplan-Meier estimates via polarstate. For secondary_cox, it fits a secondary cause-specific Cox model on complementary log-log transformed predictions (log(-log(1-p))), predicting cumulative incidence at horizon t matching the methodology in Austin et al. 2020 (pmcalibration) and McLernon et al. 2023. Unit tests pass verifying numerical behavior and bounds across single/multiple models and competing risk scenarios.