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: plotly 6.7.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-11
"""
import os
import sys
# Fix import issue: remove script directory from path before importing plotly
try:
_script_dir = os.path.dirname(os.path.abspath(__file__))
except NameError:
_script_dir = os.getcwd()
if _script_dir in sys.path:
sys.path.remove(_script_dir)
if "" in sys.path:
sys.path.remove("")
import numpy as np
import plotly.graph_objects as go
# Ensure we save to the script's directory
SCRIPT_DIR = _script_dir if "_script_dir" in locals() else os.path.dirname(os.path.abspath(__file__))
def hex_to_rgba(hex_color, alpha):
hex_color = hex_color.lstrip("#")
r = int(hex_color[0:2], 16)
g = int(hex_color[2:4], 16)
b = int(hex_color[4:6], 16)
return f"rgba({r}, {g}, {b}, {alpha})"
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
GRID = "rgba(26,26,23,0.10)" if THEME == "light" else "rgba(240,239,232,0.10)"
# Okabe-Ito palette
COLOR_A = "#009E73" # First series (bluish green)
COLOR_B = "#C475FD" # Second series (vermillion)
# Data - Clinical trial with two treatment groups
np.random.seed(42)
# Treatment A (experimental drug) - better survival
n_a = 80
time_a = np.random.exponential(scale=24, size=n_a)
time_a = np.clip(time_a, 1, 48)
event_a = np.random.binomial(1, 0.7, size=n_a)
censored_idx = time_a > 36
event_a[censored_idx] = 0
# Treatment B (standard care) - worse survival
n_b = 80
time_b = np.random.exponential(scale=16, size=n_b)
time_b = np.clip(time_b, 1, 48)
event_b = np.random.binomial(1, 0.8, size=n_b)
censored_idx = time_b > 36
event_b[censored_idx] = 0
# Kaplan-Meier estimator for Treatment A
order_a = np.argsort(time_a)
time_a_sorted = time_a[order_a]
event_a_sorted = event_a[order_a]
unique_times_a = np.unique(time_a_sorted[event_a_sorted == 1])
km_times_a = []
km_survival_a = []
km_ci_lower_a = []
km_ci_upper_a = []
S_a = 1.0
var_sum_a = 0.0
for t in unique_times_a:
at_risk = np.sum(time_a_sorted >= t)
events = np.sum((time_a_sorted == t) & (event_a_sorted == 1))
if at_risk > 0:
S_a = S_a * (1 - events / at_risk)
if at_risk > events:
var_sum_a += events / (at_risk * (at_risk - events))
if S_a > 0 and S_a < 1 and var_sum_a > 0:
se_log = np.sqrt(var_sum_a) / abs(np.log(S_a))
lower = S_a ** np.exp(1.96 * se_log)
upper = S_a ** np.exp(-1.96 * se_log)
else:
lower = max(0, S_a - 0.1)
upper = min(1, S_a + 0.1)
km_times_a.append(t)
km_survival_a.append(S_a)
km_ci_lower_a.append(max(0, lower))
km_ci_upper_a.append(min(1, upper))
km_times_a = np.array(km_times_a)
km_survival_a = np.array(km_survival_a)
km_ci_lower_a = np.array(km_ci_lower_a)
km_ci_upper_a = np.array(km_ci_upper_a)
# Kaplan-Meier estimator for Treatment B
order_b = np.argsort(time_b)
time_b_sorted = time_b[order_b]
event_b_sorted = event_b[order_b]
unique_times_b = np.unique(time_b_sorted[event_b_sorted == 1])
km_times_b = []
km_survival_b = []
km_ci_lower_b = []
km_ci_upper_b = []
S_b = 1.0
var_sum_b = 0.0
for t in unique_times_b:
at_risk = np.sum(time_b_sorted >= t)
events = np.sum((time_b_sorted == t) & (event_b_sorted == 1))
if at_risk > 0:
S_b = S_b * (1 - events / at_risk)
if at_risk > events:
var_sum_b += events / (at_risk * (at_risk - events))
if S_b > 0 and S_b < 1 and var_sum_b > 0:
se_log = np.sqrt(var_sum_b) / abs(np.log(S_b))
lower = S_b ** np.exp(1.96 * se_log)
upper = S_b ** np.exp(-1.96 * se_log)
else:
lower = max(0, S_b - 0.1)
upper = min(1, S_b + 0.1)
km_times_b.append(t)
km_survival_b.append(S_b)
km_ci_lower_b.append(max(0, lower))
km_ci_upper_b.append(min(1, upper))
km_times_b = np.array(km_times_b)
km_survival_b = np.array(km_survival_b)
km_ci_lower_b = np.array(km_ci_lower_b)
km_ci_upper_b = np.array(km_ci_upper_b)
# Create step coordinates for Treatment A
x_step_a = [0.0]
y_step_a = [1.0]
y_lower_a = [1.0]
y_upper_a = [1.0]
for i, t in enumerate(km_times_a):
prev_surv = 1.0 if i == 0 else km_survival_a[i - 1]
prev_lower = 1.0 if i == 0 else km_ci_lower_a[i - 1]
prev_upper = 1.0 if i == 0 else km_ci_upper_a[i - 1]
x_step_a.extend([t, t])
y_step_a.extend([prev_surv, km_survival_a[i]])
y_lower_a.extend([prev_lower, km_ci_lower_a[i]])
y_upper_a.extend([prev_upper, km_ci_upper_a[i]])
x_step_a = np.array(x_step_a)
y_step_a = np.array(y_step_a)
y_lower_a = np.array(y_lower_a)
y_upper_a = np.array(y_upper_a)
# Create step coordinates for Treatment B
x_step_b = [0.0]
y_step_b = [1.0]
y_lower_b = [1.0]
y_upper_b = [1.0]
for i, t in enumerate(km_times_b):
prev_surv = 1.0 if i == 0 else km_survival_b[i - 1]
prev_lower = 1.0 if i == 0 else km_ci_lower_b[i - 1]
prev_upper = 1.0 if i == 0 else km_ci_upper_b[i - 1]
x_step_b.extend([t, t])
y_step_b.extend([prev_surv, km_survival_b[i]])
y_lower_b.extend([prev_lower, km_ci_lower_b[i]])
y_upper_b.extend([prev_upper, km_ci_upper_b[i]])
x_step_b = np.array(x_step_b)
y_step_b = np.array(y_step_b)
y_lower_b = np.array(y_lower_b)
y_upper_b = np.array(y_upper_b)
# Create figure
fig = go.Figure()
# Treatment A - Confidence interval band
fig.add_trace(
go.Scatter(
x=np.concatenate([x_step_a, x_step_a[::-1]]),
y=np.concatenate([y_upper_a, y_lower_a[::-1]]),
fill="toself",
fillcolor=hex_to_rgba(COLOR_A, 0.15),
line=dict(color="rgba(0,0,0,0)"),
showlegend=False,
hoverinfo="skip",
name="CI A",
)
)
# Treatment B - Confidence interval band
fig.add_trace(
go.Scatter(
x=np.concatenate([x_step_b, x_step_b[::-1]]),
y=np.concatenate([y_upper_b, y_lower_b[::-1]]),
fill="toself",
fillcolor=hex_to_rgba(COLOR_B, 0.15),
line=dict(color="rgba(0,0,0,0)"),
showlegend=False,
hoverinfo="skip",
name="CI B",
)
)
# Treatment A - Survival curve
fig.add_trace(
go.Scatter(
x=x_step_a,
y=y_step_a,
mode="lines",
line=dict(color=COLOR_A, width=4),
name="Treatment A (Experimental)",
hovertemplate="Time: %{x:.1f} months<br>Survival: %{y:.1%}<extra></extra>",
)
)
# Treatment B - Survival curve
fig.add_trace(
go.Scatter(
x=x_step_b,
y=y_step_b,
mode="lines",
line=dict(color=COLOR_B, width=4),
name="Treatment B (Standard Care)",
hovertemplate="Time: %{x:.1f} months<br>Survival: %{y:.1%}<extra></extra>",
)
)
# Censored observations - Treatment A
censored_times_a = time_a[event_a == 0]
censored_surv_a = []
for t in censored_times_a:
idx = np.searchsorted(km_times_a, t)
if idx > 0:
censored_surv_a.append(km_survival_a[idx - 1])
else:
censored_surv_a.append(1.0)
fig.add_trace(
go.Scatter(
x=censored_times_a,
y=censored_surv_a,
mode="markers",
marker=dict(symbol="line-ns", size=14, line=dict(width=3, color=COLOR_A)),
name="Censored",
legendgroup="censored",
hovertemplate="Censored at: %{x:.1f} months<extra></extra>",
)
)
# Censored observations - Treatment B
censored_times_b = time_b[event_b == 0]
censored_surv_b = []
for t in censored_times_b:
idx = np.searchsorted(km_times_b, t)
if idx > 0:
censored_surv_b.append(km_survival_b[idx - 1])
else:
censored_surv_b.append(1.0)
fig.add_trace(
go.Scatter(
x=censored_times_b,
y=censored_surv_b,
mode="markers",
marker=dict(symbol="line-ns", size=14, line=dict(width=3, color=COLOR_B)),
name="Censored",
legendgroup="censored",
showlegend=False,
hovertemplate="Censored at: %{x:.1f} months<extra></extra>",
)
)
# Calculate median survival (time when S = 0.5)
median_a = np.nan
for i, s in enumerate(km_survival_a):
if s <= 0.5:
median_a = km_times_a[i]
break
median_b = np.nan
for i, s in enumerate(km_survival_b):
if s <= 0.5:
median_b = km_times_b[i]
break
# Layout
fig.update_layout(
title=dict(
text="survival-kaplan-meier · plotly · anyplot.ai", font=dict(size=28, color=INK), x=0.5, xanchor="center"
),
xaxis=dict(
title=dict(text="Time (months)", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
range=[0, 50],
gridcolor=GRID,
showline=True,
linewidth=2,
linecolor=INK_SOFT,
zeroline=False,
),
yaxis=dict(
title=dict(text="Survival Probability", font=dict(size=22, color=INK)),
tickfont=dict(size=18, color=INK_SOFT),
tickformat=".0%",
range=[0, 1.05],
gridcolor=GRID,
showline=True,
linewidth=2,
linecolor=INK_SOFT,
zeroline=False,
),
legend=dict(
x=0.98,
y=0.98,
xanchor="right",
yanchor="top",
font=dict(size=18, color=INK_SOFT),
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
),
paper_bgcolor=PAGE_BG,
plot_bgcolor=PAGE_BG,
margin=dict(l=100, r=80, t=100, b=100),
)
# Add median survival annotation
median_a_str = f"{median_a:.1f}" if not np.isnan(median_a) else "N/A"
median_b_str = f"{median_b:.1f}" if not np.isnan(median_b) else "N/A"
median_text = f"Median Survival:<br>Treatment A: {median_a_str} months<br>Treatment B: {median_b_str} months"
fig.add_annotation(
x=0.02,
y=0.02,
xref="paper",
yref="paper",
text=median_text,
showarrow=False,
font=dict(size=16, color=INK),
align="left",
bgcolor=ELEVATED_BG,
bordercolor=INK_SOFT,
borderwidth=1,
borderpad=8,
)
# Save
fig.write_image(os.path.join(SCRIPT_DIR, f"plot-{THEME}.png"), width=1600, height=900, scale=3)
fig.write_html(os.path.join(SCRIPT_DIR, f"plot-{THEME}.html"), include_plotlyjs="cdn")
Part of Kaplan-Meier Survival Plot on anyplot.ai.