ehrapy.tools.weibull_aft

ehrapy.tools.weibull_aft(adata, duration_col, event_col, entry_col=None)[source]

Fit the Weibull accelerated failure time regression for the survival function.

The Weibull Accelerated Failure Time (AFT) survival regression model is a statistical method used to analyze time-to-event data, where the underlying assumption is that the logarithm of survival time follows a Weibull distribution. It models the survival time as an exponential function of the predictors, assuming a specific shape parameter for the distribution and allowing for accelerated or decelerated failure times based on the covariates. See https://lifelines.readthedocs.io/en/latest/fitters/regression/WeibullAFTFitter.html

Parameters:

  • adata (AnnData) – adata: AnnData object with necessary columns duration_col and event_col.

  • duration_col (str) – Name of the column in the AnnData objects that contains the subjects’ lifetimes.

  • event_col (str) – Name of the column in anndata that contains the subjects’ death observation. If left as None, assume all individuals are uncensored.

  • entry_col (str) – Column denoting when a subject entered the study, i.e. left-truncation.

Return type:

WeibullAFTFitter

Returns:

Fitted WeibullAFTFitter

Examples

>>> import ehrapy as ep
>>> adata = ep.dt.mimic_2(encoded=False)
>>> # Flip 'censor_fl' because 0 = death and 1 = censored
>>> adata[:, ["censor_flg"]].X = np.where(adata[:, ["censor_flg"]].X == 0, 1, 0)
>>> aft = ep.tl.weibull_aft(adata, "mort_day_censored", "censor_flg")