pip install lifelines
Kaplan-Meier and Nelson-Aalen¶
For readers looking for an introduction to survival analysis, it’s recommended to start at Introduction to survival analysis
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).
lifelines assumes all “deaths” are observed unless otherwise specified.
from lifelines import KaplanMeierFitter kmf = KaplanMeierFitter() kmf.fit(T, event_observed=E) # or, more succinctly, 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') ax = kmf.plot(ax=ax)
Alternatively, for many more groups and more “pandas-esque”:
ax = plt.subplot(111) kmf = KaplanMeierFitter() for name, grouped_df in df.groupby('group'): kmf.fit(grouped_df["T"], grouped_df["E"], label=name) 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.
Much more comprehensive docs are available in `Survival analysis with lifelines`_.
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 censoring 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 censoring 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 """
Much more comprehensive docs are available in Survival Regression.
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, censorings 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() """ <lifelines.CoxPHFitter: fitted with 200 observations, 11 censored> duration col = 'T' event col = 'E' number of subjects = 200 number of events = 189 log-likelihood = -807.62 time fit was run = 2019-01-27 23:11:22 UTC --- coef exp(coef) se(coef) z p -log2(p) lower 0.95 upper 0.95 var1 0.22 1.25 0.07 2.99 <0.005 8.49 0.08 0.37 var2 0.05 1.05 0.08 0.61 0.54 0.89 -0.11 0.21 var3 0.22 1.24 0.08 2.88 <0.005 7.97 0.07 0.37 --- Concordance = 0.58 Likelihood ratio test = 15.54 on 3 df, -log2(p)=9.47 """ 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: