Libraries like lifelines provide a plethora of example datasets that one can work with. However, for many tasks you need to simulate specific behaviour in survival curves.

In this post, we demonstrate a simple algorithm to generate survival data in a format comparable to the one used in the lifelines example datasets like `load_leukemia()`

.

The generation algorithm is based on the following assumptions:

- There is a strict
**survival plateau** with a given survival probability starting at a given point in time - The
**progression** from 100% survival, t=0 to the survival plateau is **approximately linear** (i.e. if you would generate an infinite number of datapoints, the survival curve would be linear) **No censoring events** shall be generated except for censoring all surviving participants at the end point of the timeline.

Code:

import numpy as np
import random
from lifelines import KaplanMeierFitter
def simulate_survival_data_linear(N, survival_plateau, t_plateau, t_end):
"""
Generate random simulated survival data using a linear model
Keyword parameters
------------------
N : integer
Number of entries to generate
survival_plateau : float
The survival probability of the survival plateau
t_plateau : float
The time point where the survival plateau starts
t_end : float
The time point where all surviving participants will be censored.
Returns
-------
A dict with "Time" and "Event" numpy arrays: 0 is censored, 1 is event
"""
data = {"Time": np.zeros(N), "Event": np.zeros(N)}
for i in range(N):
r = random.random()
if r <= survival_plateau:
# Event is censoring at the end of the time period
data["Time"][i] = t_end
data["Event"][i] = 0
else: # Event occurs
# Normalize where we are between 100% and the survival plateau
p = (r - survival_plateau) / (1 - survival_plateau)
# Linear model: Time of event linearly depends on uniformly & randomly chosen position
# in range (0...tplateau)
t = p * t_plateau
data["Time"][i] = t
data["Event"][i] = 1
return data
# Example usage
data1 = simulate_survival_data_linear(250, 0.2, 18, 24)
data2 = simulate_survival_data_linear(250, 0.4, 17.2, 24)

Given `data1`

and `data2`

(see the usage example at the end of the code) you can plot them using

# Plot bad subgroup
kmf1 = KaplanMeierFitter()
kmf1.fit(data1["Time"], event_observed=data1["Event"], label="Bad subgroup")
ax = kmf1.plot()
# Plot good subgroup
kmf2 = KaplanMeierFitter()
kmf2.fit(data2["Time"], event_observed=data2["Event"], label="Good subgroup")
ax = kmf2.plot(ax=ax)
# Set Y axis to fixed scale
ax.set_ylim([0.0, 1.0])

Thi

#### Do not want a survival plateau?

Just set `t_end = t_survival`

:

# Example usage
data1 = simulate_survival_data_linear(250, 0.2, 24, 24)
data2 = simulate_survival_data_linear(250, 0.4, 24, 24)
# Code to plot: See above

#### What happens if you have a low number of participants?

Let’s use `25`

instead of `250`

as above:

# Example usage
data1 = simulate_survival_data_linear(25, 0.2, 24, 24)
data2 = simulate_survival_data_linear(25, 0.4, 24, 24)
# Plot code: See above

Although we generated the data with the same data, the difference is much less clear in this example, especially towards the end of the timeline (note however that the data is generated randomly, so you might see a different result). You can see a large portion of the confidence intervals overlappings near `t=24`

. In other words, based on this data it is not clear that the two groups of patients are significantly different (in other words, P \geq 0.05)