NelsonAalenFitter

class lifelines.fitters.nelson_aalen_fitter.NelsonAalenFitter(alpha=0.05, nelson_aalen_smoothing=True, **kwargs)

Class for fitting the Nelson-Aalen estimate for the cumulative hazard.

NelsonAalenFitter(alpha=0.05, nelson_aalen_smoothing=True)

Parameters:
  • alpha (float, optional (default=0.05)) – The alpha value associated with the confidence intervals.

  • nelson_aalen_smoothing (bool, optional) – If the event times are naturally discrete (like discrete years, minutes, etc.) then it is advisable to turn this parameter to False. See [1], pg.84.

Notes

[1] Aalen, O., Borgan, O., Gjessing, H., 2008. Survival and Event History Analysis

cumulative_hazard_

The estimated cumulative hazard (with custom timeline if provided)

Type:

DataFrame

confidence_interval_

The lower and upper confidence intervals for the cumulative hazard

Type:

DataFrame

durations

The durations provided

Type:

array

event_observed

The event_observed variable provided

Type:

array

timeline

The time line to use for plotting and indexing

Type:

array

entry

The entry array provided, or None

Type:

array or None

event_table

A summary of the life table

Type:

DataFrame

property conditional_time_to_event_

Return a DataFrame, with index equal to survival_function_, that estimates the median duration remaining until the death event, given survival up until time t. For example, if an individual exists until age 1, their expected life remaining given they lived to time 1 might be 9 years.

cumulative_hazard_at_times(times, label=None) Series

Return a Pandas series of the predicted cumhaz value at specific times

Parameters:

times (iterable or float)

Return type:

pd.Series

fit(durations, event_observed=None, timeline=None, entry=None, label=None, alpha=None, ci_labels=None, weights=None, fit_options=None)
Parameters:
  • durations (an array, or pd.Series, of length n) – duration subject was observed for

  • timeline (iterable) – return the best estimate at the values in timelines (positively increasing)

  • event_observed (an array, or pd.Series, of length n) – True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None

  • entry (an array, or pd.Series, of length n) – relative time when a subject entered the study. This is useful for left-truncated observations, i.e the birth event was not observed. If None, defaults to all 0 (all birth events observed.)

  • label (string) – a string to name the column of the estimate.

  • alpha (float) – the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.

  • ci_labels (iterable) – 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 (n array, or pd.Series, of length n) – 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 – Not used

Return type:

self, with new properties like cumulative_hazard_.

percentile(p)

Return the unique time point, t, such that S(t) = p.

Parameters:

p (float)

plot_hazard(bandwidth=None, **kwargs)
smoothed_hazard_(bandwidth)
Parameters:

bandwidth (float) – the bandwidth used in the Epanechnikov kernel.

Returns:

a DataFrame of the smoothed hazard

Return type:

DataFrame

smoothed_hazard_confidence_intervals_(bandwidth, hazard_=None)
Parameters:
  • bandwidth (float) – the bandwidth to use in the Epanechnikov kernel. > 0

  • hazard_ (numpy array) – a computed (n,) numpy array of estimated hazard rates. If none, uses smoothed_hazard_