pip (see its documentation if it is not yet installed on your system):
pip install lifelines
Kaplan-Meier and Nelson-Aalen¶
Let’s start by importing some data. We need the durations that individuals are observed for, and whether they “died” or not.
from lifelines.datasets import load_waltons df = load_waltons() # returns a Pandas DataFrame print(df.head()) """ T E group 0 6 1 miR-137 1 13 1 miR-137 2 13 1 miR-137 3 13 1 miR-137 4 19 1 miR-137 """ T = df['T'] E = df['E']
T is an array of durations,
E is a either boolean or binary array representing whether the “death” was observed (alternatively an individual can be censored).
By default, lifelines assumes all “deaths” are observed.
from lifelines import KaplanMeierFitter kmf = KaplanMeierFitter() kmf.fit(T, event_observed=E) # or, more succiently, kmf.fit(T, E)
After calling the
fit method, we have access to new properties like
survival_function_ and methods like
plot(). The latter is a wrapper around Panda’s internal plotting library.
kmf.survival_function_ kmf.median_ kmf.plot()
groups = df['group'] ix = (groups == 'miR-137') kmf.fit(T[~ix], E[~ix], label='control') ax = kmf.plot() kmf.fit(T[ix], E[ix], label='miR-137') kmf.plot(ax=ax)
Similar functionality exists for the
from lifelines import NelsonAalenFitter naf = NelsonAalenFitter() naf.fit(T, event_observed=E)
but instead of a
survival_function_ being exposed, a
Similar to Scikit-Learn, all statistically estimated quantities append an underscore to the property name.
Getting Data in The Right Format¶
Often you’ll have data that looks like:
Lifelines has some utility functions to transform this dataset into duration and censorship vectors:
from lifelines.utils import datetimes_to_durations # start_times is a vector of datetime objects # end_times is a vector of (possibly missing) datetime objects. T, E = datetimes_to_durations(start_times, end_times, freq='h')
Alternatively, perhaps you are interested in viewing the survival table given some durations and censorship vectors.
from lifelines.utils import survival_table_from_events table = survival_table_from_events(T, E) print(table.head()) """ removed observed censored entrance at_risk event_at 0 0 0 0 163 163 6 1 1 0 0 163 7 2 1 1 0 162 9 3 3 0 0 160 13 3 3 0 0 157 """
While the above
NelsonAalenFitter are useful, they only give us an “average” view of the population. Often we have specific data at the individual level, either continuous or categorical, that we would like to use. For this, we turn to survival regression, specifically
from lifelines.datasets import load_regression_dataset regression_dataset = load_regression_dataset() regression_dataset.head()
The input of the
fit method’s API in a regression is different. All the data, including durations, censorships and covariates must be contained in a Pandas DataFrame (yes, it must be a DataFrame). The duration column and event occurred column must be specified in the call to
from lifelines import CoxPHFitter # Using Cox Proportional Hazards model cph = CoxPHFitter() cph.fit(regression_dataset, 'T', event_col='E') cph.print_summary() """ n=200, number of events=189 coef exp(coef) se(coef) z p lower 0.95 upper 0.95 var1 0.2213 1.2477 0.0743 2.9796 0.0029 0.0757 0.3669 ** var2 0.0509 1.0522 0.0829 0.6139 0.5393 -0.1116 0.2134 var3 0.2186 1.2443 0.0758 2.8836 0.0039 0.0700 0.3672 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Concordance = 0.580 """ cph.plot()
If we focus on Aalen’s Additive model,
# Using Aalen's Additive model from lifelines import AalenAdditiveFitter aaf = AalenAdditiveFitter(fit_intercept=False) aaf.fit(regression_dataset, 'T', event_col='E')
CoxPHFitter, after fitting you’ll have access to properties like
cumulative_hazards_ and methods like
predict_survival_function. The latter two methods require an additional argument of individual covariates:
X = regression_dataset.drop(['E', 'T'], axis=1) aaf.predict_survival_function(X.iloc[10:12]).plot() # get the unique survival functions of two subjects
Like the above estimators, there is also a built-in plotting method: