Create kaplanmeier plot

In the following example we load the patient dataset and create the kaplanmeier plot, and compute the log-rank test.

# Import library
import kaplanmeier as km

# Import example data
df = km.example_data()

# Data
time_event = df['time']
censoring = df['Died']
y = df['group']

print(df)
#       time  Died  group
# 0     485     0      1
# 1     526     1      2
# 2     588     1      2
# 3     997     0      1
# 4     426     1      1
# ..    ...   ...    ...
# 175   183     0      1
# 176  3196     0      1
# 177   457     1      2
# 178  2100     1      1
# 179   376     0      1
#
# [180 rows x 3 columns]

# Compute Survival
results = km.fit(time_event, censoring, y)

# Plot
km.plot(results)
kaplanmeier plot.

fig1

Change colormap and confidence intervals

# Plot
km.plot(results, cmap='Set1', cii_lines='dense', cii_alpha=0.05)
kaplanmeier plot.

fig3

Custom colormap

# Plot
km.plot(results, cmap=[(1, 0, 1),(0, 1, 1)])
kaplanmeier plot.

fig4

Use custom kaplanmeier method

# Plot
km.plot(results, cmap='Set2', methodtype='custom')
kaplanmeier plot.

fig6

Save plot

# Save Plot
fig, ax = km.plot(results, cmap='Set2', savepath=r'c:/temp/fig1.png')
# Do not show Plot but directly save on disk
fig, ax = km.plot(results, cmap='Set2', savepath=r'c:/temp/fig1.png', visible=False)
# Increase figure resolution
fig, ax = km.plot(results, cmap='Set2', savepath=r'c:/temp/fig1.png', visible=False, dpi=300)