Python uses Statsmodes for survival analysis, covariate analysis, and other survival data analysis
Before using Statsmodels for survival data analysis, it is necessary to first install Statsmodels and related class libraries. You can use the following command to install:
pip install statsmodels
Statsmodes is a Python library that provides functions for statistical modeling and inference. It includes many statistical models for linear regression, time series analysis, hypothesis testing, and more. In Statsmodes, survival analysis is implemented through the 'lifelines' class library.
Before conducting survival data analysis, the necessary class libraries need to be imported first:
python
import pandas as pd
import numpy as np
from lifelines import CoxPHFitter
Next, if there is a downloadable dataset, you can use the 'pandas' library to load the dataset. For example, we can use the 'lifelines' built-in dataset' waltons' as a sample dataset:
python
from lifelines.datasets import load_waltons
`The Waltons' dataset contains survival data for 137 pastors from two churches in Yorkshire in the late 19th century.
python
Data=load_ Waltons() # Load Dataset
Print (data. head()) # Print the first few lines of data
Each row of the dataset represents an observation data point, which contains information about the observation time and whether the event occurred.
After completing the preparation work, the survival data analysis model can be implemented. The following is a complete sample code that uses the Cox proportional risk regression model to analyze the Waltons dataset:
python
import pandas as pd
import numpy as np
from lifelines import CoxPHFitter
from lifelines.datasets import load_waltons
#Import Dataset
data = load_waltons()
print(data.head())
#Create a CoxPHFitter instance
cph = CoxPHFitter()
#Fitting model
cph.fit(data, 'T', event_col='E')
#Print the coefficients of the model
print(cph.summary)
In this example, we first imported the 'CoxPHFitter' class from the 'lifelines' library, and then loaded the Waltons dataset. Next, we created an instance of 'CoxPHFitter' and used the 'fit' method to fit the model. Finally, we printed the coefficients of the model.
