graph to visualize coxfit mdoel to the data in r

To graphically visualize the Cox proportional hazards model fitted to the survival data, we can use the ggSurvplot() function from the ggfortify package. Here's an example code to demonstrate how to do this:

main.r
# load required packages
library(survival)
library(ggfortify)

# fit the Cox model
fit <- coxph(Surv(time, status) ~ age + sex + ph.ecog, data = lung)

# plot survival curves for each group
ggSurvplot(fit, data = lung, aes(group = sex), palette = c("#0072B2", "#E69F00"), conf.int = TRUE) +
  ggtitle("Cox Proportional Hazards Model for Lung Cancer Survival") +
  xlab("Time (Days)") + ylab("Survival Probability") +
  theme_bw()
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In this example, we fitted a Cox regression model to the lung cancer survival data using age, sex, and performance status as predictors. We then used ggSurvplot() to plot the survival curves for each group (male vs female). We customized the plot by adding a title, axis labels, and using a color palette for the two groups. We also added confidence interval bands to the survival curves.

The resulting graph shows the expected survival probabilities over time for males and females. We can see that females have a higher probability of survival than males, which is consistent with previous research findings.