lifelines

0.30.3 · active · verified Fri Apr 10

lifelines is a comprehensive Python library for survival analysis, offering implementations of various models including Kaplan-Meier, Nelson-Aalen, and Cox proportional hazards regression. It is actively maintained with regular releases, providing tools for handling right, left, and interval censored data, and includes internal plotting methods for easy visualization. The current version is 0.30.3.

Warnings

breaking The `sklean_adaptor` module was removed in version 0.28.0. There is no direct replacement, simplifying the library's API.
Fix: Remove any dependencies on `sklean_adaptor`. If scikit-learn compatibility is needed, consider `scikit-survival` or manual integration.
breaking Minimum Python version requirements have increased. Version 0.28.0 dropped support for Python < 3.9, and version 0.30.3 requires Python >= 3.11.
Fix: Ensure your Python environment is running version 3.11 or higher to use the latest `lifelines` releases.
deprecated The `initial_beta` parameter in `CoxPHFitter.fit()` was renamed to `initial_point` to align with a more general concept across models.
Fix: Update calls from `initial_beta=...` to `initial_point=...` when fitting CoxPH models.
deprecated The `plot_covariate_groups` method was renamed to `plot_partial_effects_on_outcome` and its behavior for transformed variables changed with the introduction of R-like formulas.
Fix: Use `plot_partial_effects_on_outcome` instead. Review the documentation for its usage, especially with formula-based models, as its behavior for transformed variables is different.
gotcha When using `CoxPHFitter`, avoid including a column of all 1s (an explicit intercept) in your DataFrame or formula. The Cox model implicitly handles a baseline, and an explicit intercept can lead to warnings or convergence errors.
Fix: Do not manually add an intercept column. `lifelines` handles the baseline hazard implicitly. For example, if using R-like formulas, simply list covariates without adding a `1`.

Install

pip install lifelines Install with pip

Imports

KaplanMeierFitter
```
from lifelines import KaplanMeierFitter
```
CoxPHFitter
```
from lifelines import CoxPHFitter
```
WeibullFitter
```
from lifelines import WeibullFitter
```
NelsonAalenFitter
```
from lifelines import NelsonAalenFitter
```

Quickstart

This quickstart demonstrates fitting a Kaplan-Meier survival model and a Cox Proportional Hazards regression model. It generates synthetic duration and event data, then visualizes the Kaplan-Meier survival curve and prints a summary for the Cox model.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from lifelines import KaplanMeierFitter, CoxPHFitter

# --- Kaplan-Meier Fitter Example ---
np.random.seed(42)
num_samples = 100
T = np.random.exponential(scale=10, size=num_samples) # Durations
E = np.random.binomial(n=1, p=0.7, size=num_samples)  # Events (0=censored, 1=event)

kmf = KaplanMeierFitter()
kmf.fit(T, event_observed=E, label="Sample Survival Function")

print("Kaplan-Meier Survival Function (first 5 rows):\n", kmf.survival_function_.head())
kmf.plot_survival_function()
plt.title("Kaplan-Meier Survival Estimate")
plt.xlabel("Time")
plt.ylabel("Survival Probability")
plt.grid(True)
plt.show()

# --- Cox Proportional Hazards Fitter Example ---
data = pd.DataFrame({
    'T': T, # Duration
    'E': E, # Event observed
    'age': np.random.randint(20, 70, num_samples),
    'sex': np.random.choice(, num_samples, p=[0.55, 0.45])
})

cph = CoxPHFitter()
cph.fit(data, duration_col='T', event_col='E', formula="age + sex")

print("\nCoxPHFitter Summary:\n")
cph.print_summary()

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