Code implementing "A Calibration Metric for Risk Scores with Survival Data" (MLHC 2019)
The main implementation is in Python. See semiparametric_calibration_error.py. With an object constructed with the appropriate hyperparameters, call the calculate_miscalibration_crossfit with the X the predicted probability, Y the study time outcome, and D a binary variable indicating 1 for an event or 0 if the observation is censored.
This calls the Python implementation using reticulate. Assumes you are calling this from the one directory up (so that this repo can be submodule'd into another project). The import on line 4 can be adjusted for different usage patterns. Use semiparametric_censored_miscalibration to construct a Python object and pass this object, as well as the data using the same signature as above to calculate_miscalibration to compute the calibration error.