A Kaplan-Meier survival plot visualizes the probability of survival (or event-free time) over a time period using a step function. It is the standard method for estimating survival functions from time-to-event data, handling censored observations where the event has not yet occurred. The plot shows how survival probability decreases over time, with optional confidence intervals and comparison between groups.
""" anyplot.ai
survival-kaplan-meier: Kaplan-Meier Survival Plot
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 72/100 | Updated: 2026-05-11
"""
import matplotlib.pyplot as plt
import numpy as np
# Data - Clinical trial survival data for two treatment groups
np.random.seed(42)
# Generate realistic survival times (exponential-like distribution)
n_per_group = 80
# Treatment group (better survival)
treatment_times = np.random.exponential(scale=24, size=n_per_group)
treatment_times = np.clip(treatment_times, 0.5, 60) # Cap at 60 months
treatment_censored = np.random.binomial(1, 0.25, n_per_group) # 25% censored
treatment_events = 1 - treatment_censored
# Control group (worse survival)
control_times = np.random.exponential(scale=16, size=n_per_group)
control_times = np.clip(control_times, 0.5, 60)
control_censored = np.random.binomial(1, 0.20, n_per_group) # 20% censored
control_events = 1 - control_censored
# Kaplan-Meier calculation for Treatment group
order = np.argsort(treatment_times)
treat_t_sorted = treatment_times[order]
treat_e_sorted = treatment_events[order]
treat_unique = np.unique(treat_t_sorted[treat_e_sorted == 1])
treat_time_pts = [0.0]
treat_surv_probs = [1.0]
treat_std_errs = [0.0]
treat_surv = 1.0
treat_var = 0.0
for t in treat_unique:
n_risk = np.sum(treat_t_sorted >= t)
d = np.sum((treat_t_sorted == t) & (treat_e_sorted == 1))
if n_risk > 0 and d > 0:
treat_surv *= (n_risk - d) / n_risk
if n_risk > d:
treat_var += d / (n_risk * (n_risk - d))
treat_time_pts.append(t)
treat_surv_probs.append(treat_surv)
treat_std_errs.append(np.sqrt(treat_var) * treat_surv if treat_surv > 0 else 0)
treat_times_km = np.array(treat_time_pts)
treat_surv_km = np.array(treat_surv_probs)
treat_se_km = np.array(treat_std_errs)
# Kaplan-Meier calculation for Control group
order = np.argsort(control_times)
ctrl_t_sorted = control_times[order]
ctrl_e_sorted = control_events[order]
ctrl_unique = np.unique(ctrl_t_sorted[ctrl_e_sorted == 1])
ctrl_time_pts = [0.0]
ctrl_surv_probs = [1.0]
ctrl_std_errs = [0.0]
ctrl_surv = 1.0
ctrl_var = 0.0
for t in ctrl_unique:
n_risk = np.sum(ctrl_t_sorted >= t)
d = np.sum((ctrl_t_sorted == t) & (ctrl_e_sorted == 1))
if n_risk > 0 and d > 0:
ctrl_surv *= (n_risk - d) / n_risk
if n_risk > d:
ctrl_var += d / (n_risk * (n_risk - d))
ctrl_time_pts.append(t)
ctrl_surv_probs.append(ctrl_surv)
ctrl_std_errs.append(np.sqrt(ctrl_var) * ctrl_surv if ctrl_surv > 0 else 0)
ctrl_times_km = np.array(ctrl_time_pts)
ctrl_surv_km = np.array(ctrl_surv_probs)
ctrl_se_km = np.array(ctrl_std_errs)
# Get censored observation times for tick marks
treat_censor_times = treatment_times[treatment_events == 0]
ctrl_censor_times = control_times[control_events == 0]
# Interpolate survival at censored times for tick marks
treat_censor_surv = np.interp(treat_censor_times, treat_times_km, treat_surv_km)
ctrl_censor_surv = np.interp(ctrl_censor_times, ctrl_times_km, ctrl_surv_km)
# Plot
fig, ax = plt.subplots(figsize=(16, 9))
# Python colors
treatment_color = "#306998"
control_color = "#FFD43B"
# Treatment group curve with CI
ax.step(treat_times_km, treat_surv_km, where="post", color=treatment_color, linewidth=3, label="Treatment Group")
treat_upper = np.clip(treat_surv_km + 1.96 * treat_se_km, 0, 1)
treat_lower = np.clip(treat_surv_km - 1.96 * treat_se_km, 0, 1)
ax.fill_between(treat_times_km, treat_lower, treat_upper, step="post", alpha=0.2, color=treatment_color)
# Control group curve with CI
ax.step(ctrl_times_km, ctrl_surv_km, where="post", color=control_color, linewidth=3, label="Control Group")
ctrl_upper = np.clip(ctrl_surv_km + 1.96 * ctrl_se_km, 0, 1)
ctrl_lower = np.clip(ctrl_surv_km - 1.96 * ctrl_se_km, 0, 1)
ax.fill_between(ctrl_times_km, ctrl_lower, ctrl_upper, step="post", alpha=0.3, color=control_color)
# Censored observation tick marks
ax.scatter(treat_censor_times, treat_censor_surv, marker="|", s=400, color=treatment_color, linewidth=2, zorder=5)
ax.scatter(ctrl_censor_times, ctrl_censor_surv, marker="|", s=400, color="#CC9A00", linewidth=2, zorder=5)
# Calculate median survival times
treat_median_idx = np.where(treat_surv_km <= 0.5)[0]
ctrl_median_idx = np.where(ctrl_surv_km <= 0.5)[0]
treat_median = treat_times_km[treat_median_idx[0]] if len(treat_median_idx) > 0 else None
ctrl_median = ctrl_times_km[ctrl_median_idx[0]] if len(ctrl_median_idx) > 0 else None
# Add median survival annotation lines
if treat_median:
ax.axhline(y=0.5, color="gray", linestyle=":", linewidth=1.5, alpha=0.5)
ax.axvline(x=treat_median, color=treatment_color, linestyle=":", linewidth=1.5, alpha=0.7)
if ctrl_median:
ax.axvline(x=ctrl_median, color="#CC9A00", linestyle=":", linewidth=1.5, alpha=0.7)
# Add median text
median_text = ""
if treat_median:
median_text += f"Treatment median: {treat_median:.1f} mo"
if ctrl_median:
median_text += f"\nControl median: {ctrl_median:.1f} mo"
ax.text(
0.98,
0.02,
median_text.strip(),
transform=ax.transAxes,
fontsize=16,
verticalalignment="bottom",
horizontalalignment="right",
bbox={"boxstyle": "round,pad=0.5", "facecolor": "white", "edgecolor": "gray", "alpha": 0.8},
)
# Styling
ax.set_xlabel("Time (months)", fontsize=20)
ax.set_ylabel("Survival Probability", fontsize=20)
ax.set_title("survival-kaplan-meier · matplotlib · pyplots.ai", fontsize=24)
ax.tick_params(axis="both", labelsize=16)
ax.set_xlim(0, 65)
ax.set_ylim(0, 1.05)
ax.legend(fontsize=16, loc="upper right")
ax.grid(True, alpha=0.3, linestyle="--")
plt.tight_layout()
plt.savefig("plot.png", dpi=300, bbox_inches="tight")
Part of Kaplan-Meier Survival Plot on anyplot.ai.