BreslowFlemingHarringtonFitter¶

class
lifelines.fitters.breslow_fleming_harrington_fitter.
BreslowFlemingHarringtonFitter
(alpha: float = 0.05, label: str = None)¶ Bases:
lifelines.fitters.NonParametricUnivariateFitter
Class for fitting the BreslowFlemingHarrington 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 NelsonAalen estimator is the negative logarithm of the BreslowFlemingHarrington 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 the {0} of two {1} objects.
Parameters: other (same object as self)

fit
(durations, event_observed=None, timeline=None, entry=None, label=None, alpha=None, ci_labels=None, weights=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 (rightcensored). 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 lefttruncated 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 length2 list: [<lowerbound name>, <upperbound name>]. Default: <label>_lower_<alpha>
Returns: Return type: self, with new properties like
survival_function_
.

hazard_at_times
(times, label=None)¶

median_survival_time_
¶ Return the unique time point, t, such that S(t) = 0.5. This is the “halflife” 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: Falseloc (slice) – specify a timebased 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 locationbased 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 {0} 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 (KaplanMeier, NelsonAalen, etc), turning this to True will use an linear interpolation method to provide a more “smooth” answer.

subtract
(other) → pandas.core.frame.DataFrame¶ Subtract the {0} of two {1} objects.
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)
