BreslowFlemingHarringtonFitter

class lifelines.fitters.breslow_fleming_harrington_fitter.BreslowFlemingHarringtonFitter(alpha: float = 0.05, label: Optional[str] = None)

Bases: lifelines.fitters.NonParametricUnivariateFitter

Class for fitting the Breslow-Fleming-Harrington estimate for the survival function. This estimator is a biased estimator of the survival function but is more stable when the population is small and there are too few early truncation times, it may happen that is the number of patients at risk and the number of deaths is the same.

Mathematically, the Nelson-Aalen estimator is the negative logarithm of the Breslow-Fleming-Harrington estimator.

Parameters:alpha (float, optional (default=0.05)) – The alpha value associated with the confidence intervals.
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_density_at_times(times, label=None)
cumulative_hazard_at_times(times, label=None)
divide(other) → pandas.core.frame.DataFrame

Divide self’s survival function from another model’s survival function.

Parameters:other (same object as self)
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 – 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, optional (default=0.05)) – 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_<alpha>
  • fit_options – Not used.
Returns:
Return type:

self, with new properties like survival_function_.
fit_right_censoring(*args, **kwargs)

Alias for fit

See also

fit()

hazard_at_times(times, label=None)
label
median_survival_time_

Return the unique time point, t, such that S(t) = 0.5. This is the “half-life” of the population, and a robust summary statistic for the population, if it exists.

percentile(p: float) → float

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

Parameters:p (float)
plot(**kwargs)

Plots a pretty figure of the model

Matplotlib plot arguments can be passed in inside the kwargs, plus

Parameters:

  • show_censors (bool) – place markers at censorship events. Default: False

  • censor_styles (dict) – If show_censors, this dictionary will be passed into the plot call.

  • ci_alpha (float) – the transparency level of the confidence interval. Default: 0.3

  • ci_force_lines (bool) – force the confidence intervals to be line plots (versus default shaded areas). Default: False

  • ci_show (bool) – show confidence intervals. Default: True

  • ci_legend (bool) – if ci_force_lines is True, this is a boolean flag to add the lines’ labels to the legend. Default: False

  • at_risk_counts (bool) – show group sizes at time points. See function add_at_risk_counts for details. Default: False

  • loc (slice) – specify a time-based subsection of the curves to plot, ex:

    >>> model.plot(loc=slice(0.,10.))

    will plot the time values between t=0. and t=10.

  • iloc (slice) – specify a location-based subsection of the curves to plot, ex:

    >>> model.plot(iloc=slice(0,10))

    will plot the first 10 time points.
Returns:

a pyplot axis object
Return type:

ax
plot_cumulative_density(**kwargs)
plot_cumulative_hazard(**kwargs)
plot_density(**kwargs)
plot_hazard(**kwargs)
plot_survival_function(**kwargs)
predict(times: Union[Iterable[float], float], interpolate=False) → pandas.core.series.Series

Predict the fitter at certain point in time. Uses a linear interpolation if points in time are not in the index.

Parameters:
  • times (scalar, or array) – a scalar or an array of times to predict the value of {0} at.
  • interpolate (bool, optional (default=False)) – for methods that produce a stepwise solution (Kaplan-Meier, Nelson-Aalen, etc), turning this to True will use an linear interpolation method to provide a more “smooth” answer.
subtract(other) → pandas.core.frame.DataFrame

Subtract self’s survival function from another model’s survival function.

Parameters:other (same object as self)
survival_function_at_times(times, label=None) → pandas.core.series.Series

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

Parameters:
  • times (iterable or float)
  • label (str)