, duration_col, event_col, entry_col=None)[source]#

Fit the log logistic accelerated failure time regression for the survival function. The Log-Logistic Accelerated Failure Time (AFT) survival regression model is a powerful statistical tool employed in the analysis of time-to-event data. This model operates under the assumption that the logarithm of survival time adheres to a log-logistic distribution, offering a flexible framework for understanding the impact of covariates on survival times. By modeling survival time as a function of predictors, the Log-Logistic AFT model enables researchers to explore how specific factors influence the acceleration or deceleration of failure times, providing valuable insights into the underlying mechanisms driving event occurrence. See

  • 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:



Fitted LogLogisticAFTFitter


>>> 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)
>>> llf =, "mort_day_censored", "censor_flg")