Cox_Regression_python_R.qmd

---
title: sample code for implement cox regression using python and R 
format: gfm
warning: false
message: false
---


```{r}
#| include: false


library(reticulate)
path <- Sys.which("python")
path <- gsub("\\", "//", path, fixed = TRUE)
use_python(path)
```




# Using R


```{r}
# if (!require(gtsummary)) {
#     chooseCRANmirror(ind = 1, graphics = FALSE)
#     install.packages("gtsummary")
#     library(gtsummary)
# }

# if (!require(stargazer)) {
#     chooseCRANmirror(ind = 1, graphics = FALSE)
#     install.packages("stargazer")
#     library(stargazer)
# }

library(survival)
lung |> names()

lung$status |> table()

dat <- na.omit(lung)
Y <- with(dat, Surv(time, status == 2))



names(dat)
Model <- coxph(Y ~ ph.ecog + ph.karno, data = dat)
Model |> summary()

# Model |> tbl_regression()
# stargazer(Model)


```



***
***

# Using Python 


```{python}
import numpy as np
import pandas as pd
from lifelines import CoxPHFitter

cph = CoxPHFitter()
dat_py = r.dat
dat_py.head()
dat2 = dat_py[['time', 'status', 'ph.ecog', 'ph.karno']]
dat2.head()

dat2['status'] = dat2['status'].replace([1, 2], [0, 1])
dat2.head()
model_cardio_cox_ph = cph.fit(df = dat2, duration_col = 'time', event_col = 'status')
model_cardio_cox_ph.print_summary()  # access the individual results using cph.summary


```