Kaplan-Meier Curve

• Python
• R
• SPSS
• SAS
• Stata

from lifelines import KaplanMeierFitter

# Initialize and fit model
kmf = KaplanMeierFitter()
kmf.fit(durations=df['time'], event_observed=df['event'])

# Plot survival curve
kmf.plot_survival_function()

# Median survival time
median_survival = kmf.median_survival_time_

library(survival)

# Fit Kaplan-Meier estimator
km_fit <- survfit(Surv(time, event) ~ 1, data=df)

# Summary
summary(km_fit)

# Plot survival curve
plot(km_fit, xlab="Time", ylab="Survival Probability", main="Kaplan-Meier Curve")

# Group comparison
km_group <- survfit(Surv(time, event) ~ group, data=df)
survdiff(Surv(time, event) ~ group, data=df)

KM time BY group
  /STATUS=event(1)
  /PRINT TABLE
  /PLOT SURVIVAL
  /TEST LOGRANK
  /COMPARE OVERALL POOLED.

PROC LIFETEST DATA=dataset METHOD=KM PLOTS=(S);
  TIME time*event(0);
  STRATA group / TEST=LOGRANK;
RUN;

stset time, failure(event)
sts graph, by(group)
sts test group, logrank

A right-censored survival dataset with time-to-event data and group indicators

• R Code
• Analysis Results

# Generate synthetic survival data
set.seed(123)
library(survival)

n <- 100
group <- rep(c("A", "B"), each=n/2)

# Generate survival times with group effect
true_times <- ifelse(group == "A", 
                   rexp(n/2, rate=0.1),
                   rexp(n/2, rate=0.2))

# Generate censoring times
cens_times <- runif(n, 5, 15)

# Create observed times and event indicator
time <- pmin(true_times, cens_times)
event <- as.numeric(true_times <= cens_times)

df <- data.frame(time, event, group)

# Fit Kaplan-Meier estimator
km_fit <- survfit(Surv(time, event) ~ group, data=df)

# Summary at time points
summary(km_fit, times=seq(0, 10, by=2))

# Plot survival curves
plot(km_fit, col=1:2, lwd=2, xlab="Time", ylab="Survival Probability",
     main="Kaplan-Meier Survival Curves")
legend("topright", legend=c("Group A", "Group B"), col=1:2, lwd=2)

# Log-rank test
survdiff(Surv(time, event) ~ group, data=df)

> summary(km_fit, times=seq(0, 10, by=2))
Call: survfit(formula = Surv(time, event) ~ group, data = df)

                group=A 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     50       0    1.000  0.0000        1.000        1.000
    2     45       5    0.900  0.0424        0.820        0.988
    4     32      13    0.740  0.0620        0.627        0.873
    6     20      12    0.560  0.0703        0.435        0.721
    8     10      10    0.360  0.0680        0.248        0.522
   10      5       5    0.180  0.0543        0.095        0.341

                group=B 
 time n.risk n.event survival std.err lower 95% CI upper 95% CI
    0     50       0    1.000  0.0000        1.000        1.000
    2     38      12    0.760  0.0604        0.649        0.890
    4     22      16    0.520  0.0707        0.397        0.681
    6     10      12    0.280  0.0636        0.178        0.441
    8      3       7    0.100  0.0426        0.043        0.234
   10      1       2    0.040  0.0277        0.010        0.160

> survdiff(Surv(time, event) ~ group, data=df)
        N Observed Expected (O-E)^2/E (O-E)^2/V
group=A 50       20     31.2      4.02      12.3
group=B 50       30     18.8      6.68      12.3

 Chisq= 12.3  on 1 degrees of freedom, p= 0.00045

Need help interpreting the results of your Kaplan-Meier Curve analysis? Our comprehensive interpretation guide explains:

• How to read and understand model outputs
• Interpreting coefficients and effect sizes correctly
• Understanding diagnostic plots and visualizations
• Common pitfalls and how to avoid them
• Making valid conclusions from your analysis