Survival Analysis in Python

In Python, there are several packages available for conducting survival analysis, including:

1. "lifelines": This package contains a wide range of survival analysis models, including Kaplan-Meier, Cox proportional hazards, and Aalen's additive model. It also has functions for data visualization and model fitting.

2. "survivalstan": This package provides an interface for fitting survival models using the Stan probabilistic programming language.

3. "pysurvival": This package provides a wide range of models for survival analysis, including parametric, non-parametric, and deep learning models. It also has feature selection, missing data imputation, and visualization capabilities.

4. "scikit-survival": This package provides a set of tools for survival analysis, including univariate and multivariate methods, and ensemble techniques.

Here is an example of how you can use the "lifelines" package to conduct a survival analysis in Python:

# load the lifelines package

from lifelines import KaplanMeierFitter

# fit a Kaplan-Meier survival estimate to the data

kmf = KaplanMeierFitter()

kmf.fit(durations, event_observed=events, label='Kaplan Meier Estimate')

# plot the survival curve

kmf.plot()

In this example, the "KaplanMeierFitter" class is used to create a Kaplan-Meier survival estimate object. The "fit" method is then used to fit the model to the data, and the "plot" method is used to visualize the results. The input for the fit method are durations and events, which are the time and the event of interest.