KaplanMeierFitter¶

class
lifelines.fitters.kaplan_meier_fitter.
KaplanMeierFitter
(alpha: float = 0.05, label: str = None)¶ Bases:
lifelines.fitters.NonParametricUnivariateFitter
Class for fitting the KaplanMeier estimate for the survival function.
Parameters:  alpha (float, optional (default=0.05)) – The alpha value associated with the confidence intervals.
 label (string, optional) – Provide a new label for the estimate  useful if looking at many groups.
Examples
from lifelines import KaplanMeierFitter from lifelines.datasets import load_waltons waltons = load_waltons() kmf = KaplanMeierFitter(label="waltons_data") kmf.fit(waltons['T'], waltons['E']) kmf.plot()

survival_function_
¶ The estimated survival function (with custom timeline if provided)
Type: DataFrame

median_survival_time_
¶ The estimated median time to event. np.inf if doesn’t exist.
Type: float

confidence_interval_
¶ The lower and upper confidence intervals for the survival function. An alias of
confidence_interval_survival_function_
. Uses Greenwood’s Exponential formula (“loglog” in R).Type: DataFrame

confidence_interval_survival_function_
¶ The lower and upper confidence intervals for the survival function. An alias of
confidence_interval_
. Uses Greenwood’s Exponential formula (“loglog” in R).Type: DataFrame

cumulative_density_
¶ The estimated cumulative density function (with custom timeline if provided)
Type: DataFrame

confidence_interval_cumulative_density_
¶ The lower and upper confidence intervals for the cumulative density.
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

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) → pandas.core.series.Series¶ Return a Pandas series of the predicted cumulative density at specific times
Parameters: times (iterable or float) Returns: Return type: pd.Series

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)¶ Fit the model to a rightcensored dataset
Parameters:  durations (an array, list, pd.DataFrame or pd.Series) – length n – duration subject was observed for
 event_observed (an array, list, pd.DataFrame, or pd.Series, optional) – True if the the death was observed, False if the event was lost (rightcensored). Defaults all True if event_observed==None
 timeline (an array, list, pd.DataFrame, or pd.Series, optional) – return the best estimate at the values in timelines (positively increasing)
 entry (an array, list, pd.DataFrame, or pd.Series, optional) – relative time when a subject entered the study. This is useful for lefttruncated (not leftcensored) observations. If None, all members of the population entered study when they were “born”.
 label (string, optional) – a string to name the column of the estimate.
 alpha (float, optional) – the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.
 ci_labels (tuple, optional) – add custom column names to the generated confidence intervals as a length2 list: [<lowerbound name>, <upperbound name>]. Default: <label>_lower_<1alpha/2>
 weights (an array, list, pd.DataFrame, or pd.Series, optional) – 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.
Returns: self – self with new properties like
survival_function_
,plot()
,median_survival_time_
Return type:

fit_interval_censoring
(lower_bound, upper_bound, event_observed=None, timeline=None, label=None, alpha=None, ci_labels=None, entry=None, weights=None, tol: float = 1e05, show_progress: bool = False) → KaplanMeierFitter¶ Fit the model to a intervalcensored dataset using nonparametric MLE. This estimator is also called the Turnbull Estimator.
Currently, only closed interval are supported. However, it’s easy to create open intervals by adding (or subtracting) a very small value from the lowerbound (or upper bound). For example, the following turns closed intervals into open intervals.
>>> left, right = df['left'], df['right'] >>> KaplanMeierFitter().fit_interval_censoring(left + 0.00001, right  0.00001)
Note
This is new and experimental, and many features are missing.
Parameters:  lower_bound (an array, list, pd.DataFrame or pd.Series) – length n – lower bound of observations
 upper_bound (an array, list, pd.DataFrame or pd.Series) – length n – upper bound of observations
 event_observed (an array, list, pd.DataFrame, or pd.Series, optional) – True if the the death was observed, False if the event was lost (rightcensored). This can be computed from the lower_bound and upper_bound, and can be left blank.
 timeline (an array, list, pd.DataFrame, or pd.Series, optional) – return the best estimate at the values in timelines (positively increasing)
 entry (an array, list, pd.DataFrame, or pd.Series, optional) – relative time when a subject entered the study. This is useful for lefttruncated (not leftcensored) observations. If None, all members of the population entered study when they were “born”.
 label (string, optional) – a string to name the column of the estimate.
 alpha (float, optional) – the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.
 ci_labels (tuple, optional) – add custom column names to the generated confidence intervals as a length2 list: [<lowerbound name>, <upperbound name>]. Default: <label>_lower_<1alpha/2>
 weights (an array, list, pd.DataFrame, or pd.Series, optional) – 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.
 tol (float, optional) – minimum difference in log likelihood changes for iterative algorithm.
 show_progress (bool, optional) – display information during fitting.
Returns: self – self with new properties like
survival_function_
,plot()
,median_survival_time_
Return type:

fit_left_censoring
(durations, event_observed=None, timeline=None, entry=None, label=None, alpha=None, ci_labels=None, weights=None)¶ Fit the model to a leftcensored dataset
Parameters:  durations (an array, list, pd.DataFrame or pd.Series) – length n – duration subject was observed for
 event_observed (an array, list, pd.DataFrame, or pd.Series, optional) – True if the the death was observed, False if the event was lost (rightcensored). Defaults all True if event_observed==None
 timeline (an array, list, pd.DataFrame, or pd.Series, optional) – return the best estimate at the values in timelines (positively increasing)
 entry (an array, list, pd.DataFrame, or pd.Series, optional) – relative time when a subject entered the study. This is useful for lefttruncated (not leftcensored) observations. If None, all members of the population entered study when they were “born”.
 label (string, optional) – a string to name the column of the estimate.
 alpha (float, optional) – the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.
 ci_labels (tuple, optional) – add custom column names to the generated confidence intervals as a length2 list: [<lowerbound name>, <upperbound name>]. Default: <label>_lower_<1alpha/2>
 weights (an array, list, pd.DataFrame, or pd.Series, optional) – 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.
Returns: self – self with new properties like
survival_function_
,plot()
,median_survival_time_
Return type:

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)¶ Plots a pretty figure of the cumulative density function.
Matplotlib plot arguments can be passed in inside the kwargs.
Parameters: show_censors (bool) – place markers at censorship events. Default: False
censor_styles (bool) – If show_censors, this dictionary will be passed into the plot call.
ci_alpha (bool) – 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_hazard
(**kwargs)¶

plot_density
(**kwargs)¶

plot_hazard
(**kwargs)¶

plot_loglogs
(*args, **kwargs)¶ Plot \(\log(\log(S(t)))\) against \(\log(t)\). Same arguments as
.plot
.

plot_survival_function
(**kwargs)¶ Alias of
plot

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) Returns: Return type: pd.Series