Survival data handler

The aim of this package is to facilitate the use of survival data by switching from temporal data in the form of a collection of survival functions to temporal matrices calculating other functions derived from survival analysis, such as residual life, hazard function, etc. analysis, such as residual life expectancy, hazard function, etc.

import pandas as pd

from lifelines import CoxPHFitter
from lifelines.datasets import load_rossi

rossi = load_rossi()
cph = CoxPHFitter()

cph.fit(rossi, duration_col='week', event_col='arrest')
curves = cph.predict_survival_function(rossi).T
curves.columns = pd.to_timedelta(curves.columns.to_numpy() * 7, unit="D")
print(curves.head())

from survival_data_handler import Lifespan


age = pd.to_timedelta(rossi["age"] * 365.25, unit="D")
birth = pd.to_datetime('2000')
rossi["index"] = rossi.index
    
birth = pd.to_datetime('2000')

lifespan = Lifespan(
    curves,
    index=rossi["index"],
    birth=birth,
    age=age,
    window=(pd.to_datetime("2000"), pd.to_datetime("2001"))
)

We now add the supervision data (in the form of duration)

lifespan.add_supervision(
    event=rossi["arrest"],                                      # True if the data is observed False, when censored
    durations=rossi["duration"] + birth      # The duration
)

Let's calculate the associated performance

lifespan.assess_metric("survival_function")