Survival analysis in Python, including Kaplan Meier, Nelson Aalen and regression
What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer why do events occur now versus later under uncertainity (where events might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like what factors might influence deaths?
But outside of medicine and actuarial science, there are many other interesting and exciting applications of this lesser-known technique, for example: - SaaS providers are interested in measuring customer lifetimes, or time to first behaviours. - sociologists are interested in measure political parties lifetimes, or relationships, or marriages - Businesses are interested in what variables affect lifetime value
lifelines is a pure Python implementation of the best parts of survival analysis. We’d love to hear if you are using lifelines, please ping me at [@cmrn_dp](https://twitter.com/Cmrn_DP) and let me know your thoughts on the library.
The usual Python data stack: Numpy, Scipy, Pandas (a modern version please), Matplotlib
You can install lifelines using
pip install lifelines
Or getting the bleeding edge version with:
pip install git+https://github.com/CamDavidsonPilon/lifelines.git
or upgrade with
pip install --upgrade git+https://github.com/CamDavidsonPilon/lifelines.git
from the command line.
Intro to lifelines and survival analysis
Situation: 500 random individuals are born at time 0, currently it is time 12, so we have possibly not observed all death events yet.
# Create lifetimes, but censor all lifetimes after time 12 censor_after = 12 actual_lifetimes = np.random.exponential(10, size=500) observed_lifetimes = np.minimum( actual_lifetimes, censor_after*np.ones(500) ) C = (actual_lifetimes < censor_after) #boolean array
Non-parametrically fit the survival curve:
from lifelines import KaplanMeierFitter kmf = KaplanMeierFitter() kmf.fit(observed_lifetimes, event_observed=C) # fitter methods have an internal plotting method. # plot the curve with the confidence intervals kmf.plot()
It looks like 50% of all individuals are dead before time 7.
print kmf.survival_function_.head() time KM-estimate 0.000000 1.000 0.038912 0.998 0.120667 0.996 0.125719 0.994 0.133778 0.992
Non-parametrically fit the cumulative hazard curve:
from lifelines import NelsonAalenFitter naf = NelsonAalenFitter() naf.fit(observed_lifetimes, event_observed=C) #plot the curve with the confidence intervals naf.plot()
print naf.cumulative_hazard_.head() time NA-estimate 0.000000 0.000000 0.038912 0.002000 0.120667 0.004004 0.125719 0.006012 0.133778 0.008024
Compare two populations using the logrank test:
from lifelines.statistics import logrank_test other_lifetimes = np.random.exponential(3, size=500) summary, p_value, results = logrank_test(observed_lifetimes, other_lifetimes, alpha=0.95) print summary Results df: 1 alpha: 0.95 t 0: -1 test: logrank null distribution: chi squared __ p-value ___|__ test statistic __|__ test results __ 0.00000 | 268.465 | True
(Less Quick) Intro to lifelines and survival analysis
If you are new to survival analysis, wondering why it is useful, or are interested in lifelines examples and syntax, please check out the Documentation and Tutorials page
Alternatively, you can use the IPython notebooks tutorials, located in the main directory of the repo:
The Feedback MIT License (FMIT)
Copyright (c) 2013, Cameron Davidson-Pilon
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
- The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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