Survival analysis is a statistical method used to analyze the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. This type of analysis is particularly useful in medical research, engineering, and social sciences.

In this tutorial, we’ll explore how to perform survival analysis using the Techtonique API. We’ll work with two datasets:

• The German Breast Cancer Study Group 2 (GBSG2) dataset
• The Kidney dataset

We’ll demonstrate how to:

1. Make API calls using curl (and other programming languages)
2. Generate survival curves (including prediction intervals) in Python using different Machine learning methods
3. Compare survival probabilities between different patients in Python

Let’s get started by downloading our datasets and making our first API call.

0 – Download data

!wget https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/survival/gbsg2_2.csv
!wget https://raw.githubusercontent.com/Techtonique/datasets/refs/heads/main/tabular/survival/kidney.csv

1 – Using curl

First, get a token from: https://www.techtonique.net/token.

Note that you can use https://curlconverter.com/ to translate the following request in your favorite programming language.

!curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "[email protected];type=text/csv" \
"https://www.techtonique.net/survivalcurve?method=km"

{"times":[2.0,4.0,5.0,6.0,7.0,8.0,9.0,12.0,13.0,15.0,16.0,17.0,22.0,23.0,24.0,25.0,26.0,27.0,28.0,30.0,34.0,38.0,39.0,40.0,43.0,46.0,53.0,54.0,58.0,63.0,66.0,70.0,78.0,96.0,108.0,113.0,114.0,119.0,130.0,132.0,141.0,149.0,152.0,154.0,156.0,159.0,177.0,185.0,190.0,196.0,201.0,245.0,292.0,318.0,333.0,402.0,447.0,511.0,536.0,562.0],"survival_probabilities":[0.9868421052631579,0.9868421052631579,0.9868421052631579,0.9868421052631579,0.9590437361008154,0.9312453669384729,0.9169185151394196,0.8882648115413128,0.8739379597422593,0.8448066944175173,0.8302410617551463,0.8154153285095187,0.8005895952638911,0.7854841312023082,0.7703786671407253,0.7549710937979108,0.7392425293437876,0.7235139648896645,0.7077854004355413,0.6448711426190487,0.6291425781649255,0.6134140137108023,0.5976854492566791,0.581956884802556,0.566228320348433,0.566228320348433,0.5500503683384778,0.5500503683384778,0.533382175358524,0.51671398237857,0.5000457893986162,0.5000457893986162,0.48280283114349154,0.46555987288836687,0.46555987288836687,0.46555987288836687,0.4469374779728322,0.42831508305729754,0.40969268814176285,0.3910702932262282,0.3724478983106935,0.3724478983106935,0.33324285638325213,0.3136403354195314,0.2940378144558107,0.2940378144558107,0.27303511342325276,0.25203241239069485,0.23102971135813694,0.21002701032557902,0.18902430929302114,0.16802160826046322,0.1470189072279053,0.12601620619534742,0.10501350516278952,0.08401080413023163,0.06300810309767371,0.04200540206511581,0.021002701032557906,0.0],"lower_bound":[0.9102553109267459,0.9102553109267459,0.9102553109267459,0.9102553109267459,0.8783094603652322,0.8426122174685124,0.8242852019907436,0.7888341667037795,0.7715955664880945,0.7371072220745135,0.7202424697504548,0.7031818232846035,0.6863446966653962,0.6692821407981255,0.6524290431612817,0.6353201691306911,0.6179396029435383,0.600766952221238,0.5837876045897149,0.5175887126537961,0.501428890533292,0.48541324827274046,0.46953746121430484,0.45379790888446325,0.4381915934086143,0.4381915934086143,0.42215113184357683,0.42215113184357683,0.4056421189555767,0.3893007306550252,0.37312389775812754,0.37312389775812754,0.3564029597193025,0.3398699645608237,0.3398699645608237,0.3398699645608237,0.3218323705853136,0.30405511656865314,0.2865346991473782,0.2692692944768051,0.25225871014379764,0.25225871014379764,0.21679278606980396,0.19954942220101346,0.18264013482404035,0.18264013482404035,0.1645274238943602,0.14689232612982656,0.12975301412650406,0.1131351703821566,0.09707385136784838,0.08161633322854538,0.06682654409885397,0.05279221487813534,0.039637008241833256,0.027542531139939947,0.01679208899904062,0.00786908624614294,0.0017150505311993823,0.0],"upper_bound":[0.9981359972257208,0.9981359972257208,0.9981359972257208,0.9981359972257208,0.9866128777383311,0.9708048016896479,0.9618157052393598,0.9425317871753408,0.9323728818964988,0.9109697137263687,0.899904851447792,0.8884763999537109,0.8768457976501994,0.8648589323229849,0.8526900734704261,0.8401623217235352,0.8272636042197831,0.8141957681239268,0.8009676532222794,0.7465892181712186,0.7326545907259948,0.7185920973665509,0.7044048145487647,0.690095344841944,0.6756658589291663,0.6756658589291663,0.6607945343186838,0.6607945343186838,0.6454449921255175,0.6299564629168295,0.6143301237450786,0.6143301237450786,0.5981519629753712,0.5818208939410109,0.5818208939410109,0.5818208939410109,0.5643573897967261,0.5466951722133224,0.5288338025067468,0.5107718445919868,0.49250683875975637,0.49250683875975637,0.4539092141443016,0.4342257363301834,0.4142753605771428,0.4142753605771428,0.3930577429935252,0.3714694433520813,0.34949101981707487,0.32709722167473304,0.3042554459039903,0.2809234582974302,0.25704596733927315,0.23254934629068258,0.2073333016806911,0.18125763912628218,0.15412324485059997,0.1256694697451091,0.09599962183907555,0.0]}

Use https://curlconverter.com/ to translate the following request in your favorite programming language.

!curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@gbsg2_2.csv;type=text/csv" \
"https://www.techtonique.net/survivalcurve?method=RidgeCV&patient_id=0"

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Use https://curlconverter.com/ to translate the following request in your favorite programming language.

!curl -X POST \
-H "Authorization: Bearer YOUR_TOKEN_HERE" \
-F "file=@gbsg2_2.csv;type=text/csv" \
"https://www.techtonique.net/survivalcurve?method=RidgeCV&patient_id=1"

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2 – Using Python

Obtain survival curves for the 4 first patients in the test set (patients with training_index=0)

import requests

headers = {
    'Authorization': 'Bearer YOUR_TOKEN_HERE',
}

responses = []

for patient_id in range(4):

  params = {
      'method': 'RidgeCV',
      'patient_id': str(patient_id),
  }

  files = {
      'file': ('gbsg2_2.csv', open('gbsg2_2.csv', 'rb'), 'text/csv'),
  }

  responses.append(requests.post('https://www.techtonique.net/survivalcurve',
                                 params=params, headers=headers, files=files).json())

Plot survival curves for the 4 first patients in the test set (patients with training_index=0) including prediction intervals

import matplotlib.pyplot as plt
fig, axes = plt.subplots(2, 2, figsize=(10, 8))

for i, response in enumerate(responses):
  row = i // 2
  col = i % 2
  axes[row, col].plot(response["times"], response["survival_probabilities"], label=f'Patient {i}')
  axes[row, col].fill_between(response["times"],
                              response["lower_bound"],
                              response["upper_bound"],
                              color='gray', alpha=0.3, label='Confidence Interval')
  axes[row, col].set_xlabel('Time')
  axes[row, col].set_ylabel('Survival Probability')
  axes[row, col].set_title(f'Survival Curve for Patient {i}')
  axes[row, col].legend()
  axes[row, col].grid(True)

plt.tight_layout()
plt.show()

Compare survival curves for the 4 first patients in the test set (patients with training_index=0)

import matplotlib.pyplot as plt

for i, response in enumerate(responses):
  plt.plot(response["times"], response["survival_probabilities"],
           label=f'Patient {i}')

plt.xlabel('Time')
plt.ylabel('Survival Probability')
plt.title('Survival Curves for 4 Patients')
plt.legend()
plt.grid(True)
plt.show()

Kaplan-Meier survival curve for the entire population

import requests

headers = {
    'Authorization': 'Bearer YOUR_TOKEN_HERE',
}

params = {
    'method': 'km',
}

files = {
    'file': ('kidney.csv', open('kidney.csv', 'rb'), 'text/csv'),
}

response = requests.post('https://www.techtonique.net/survivalcurve', params=params, headers=headers, files=files)

import json
import matplotlib.pyplot as plt

# Assuming the response object 'response' contains the survival curve data as JSON
data = response.json()

# Extract time and survival probability data
time = data['times']
survival = data['survival_probabilities']

# Plot the survival curve
plt.figure(figsize=(10, 6))
plt.step(time, survival, where='post')
plt.xlabel('Time')
plt.ylabel('Survival Probability')
plt.title('Kaplan-Meier Survival Curve')
plt.grid(True)
plt.show()