ehrapy.tools.kaplan_meier

ehrapy.tools.kaplan_meier(adata, duration_col, event_col=None, *, timeline=None, entry=None, label=None, alpha=None, ci_labels=None, weights=None, fit_options=None, censoring='right')[source]

Fit the Kaplan-Meier estimate for the survival function.

The Kaplan–Meier estimator, also known as the product limit estimator, is a non-parametric statistic used to estimate the survival function from lifetime data. In medical research, it is often used to measure the fraction of patients living for a certain amount of time after treatment.

See https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator

https://lifelines.readthedocs.io/en/latest/fitters/univariate/KaplanMeierFitter.html#module-lifelines.fitters.kaplan_meier_fitter

Parameters:

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

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

  • event_col (str | None, default: None) – The name of the column in anndata that contains the subjects’ death observation.

  • timeline (list[float] | None, default: None) – Return the best estimate at the values in timelines (positively increasing)

  • entry (str | None, default: None) – Relative time when a subject entered the study. This is useful for left-truncated (not left-censored) observations. If None, all members of the population entered study when they were “born”.

  • label (str | None, default: None) – A string to name the column of the estimate.

  • alpha (float | None, default: None) – The alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.

  • ci_labels (list[str] | None, default: None) – Add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>] (default: <label>_lower_<1-alpha/2>).

  • weights (list[float] | None, default: None) – If providing a weighted dataset. For example, instead of providing every subject as a single element of durations and event_observed, one could weigh subject differently.

  • fit_options (dict | None, default: None) – Additional keyword arguments to pass into the estimator.

  • censoring (Literal['right', 'left'], default: 'right') – ‘right’ for fitting the model to a right-censored dataset. (default, calls fit). ‘left’ for fitting the model to a left-censored dataset (calls fit_left_censoring).

Return type:

KaplanMeierFitter

Returns:

Fitted KaplanMeierFitter.

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)
>>> kmf = ep.tl.kaplan_meier(adata, "mort_day_censored", "censor_flg", label="Mortality")