Lifelines - Survival Analysis

When to Use

• Analyzing clinical trial data (time to death, disease progression).
• Comparing survival between treatment groups.
• Identifying risk factors using Cox Proportional Hazards regression.
• Building survival models for prognosis.
• Epidemiology studies (time to infection, recovery).

Core Principles

Censoring

Hazard Ratios

Survival Curves

Quick Reference

Standard Imports

from lifelines import KaplanMeierFitter, CoxPHFitter
from lifelines.statistics import logrank_test
import pandas as pd

Basic Patterns

# 1. Kaplan-Meier (Visualizing survival)
kmf = KaplanMeierFitter()
kmf.fit(durations=df['days'], event_observed=df['died'])
kmf.plot_survival_function()
kmf.median_survival_time_  # Time when 50% have died

# 2. Cox Proportional Hazards (Risk factors)
cph = CoxPHFitter()
cph.fit(df, duration_col='days', event_col='died')
cph.print_summary() # See hazard ratios for age, drug type, etc.
cph.plot_partial_effects_on_outcome(covariates=['age'], values=[30, 50, 70])

Critical Rules

✅ DO

• Use event_observed correctly - 1 = event occurred, 0 = censored.
• Check proportional hazards assumption - Use

  cph.check_assumptions()

  to validate Cox model.
• Compare groups with logrank test - Statistical test for survival curve differences.
• Plot confidence intervals - Survival estimates have uncertainty, especially with small samples.

❌ DON'T

• Don't ignore censoring - Treating censored patients as "survived" biases results.
• Don't use regular regression - Time-to-event data requires specialized methods.
• Don't assume proportional hazards - If violated, use stratified Cox or parametric models.

Advanced Patterns

Comparing Multiple Groups

from lifelines.statistics import multivariate_logrank_test

# Compare survival across treatment groups
results = multivariate_logrank_test(df['days'], df['group'], df['died'])
print(results.p_value)

Parametric Models

from lifelines import WeibullFitter, ExponentialFitter

# When you need to extrapolate beyond observed data
wf = WeibullFitter()
wf.fit(df['days'], df['died'])
wf.plot()