Survival Analysis in R

Survival analysis, also known as time-to-event analysis, is a statistical method used to analyze the time until an event of interest occurs. In R, there are several packages available for conducting survival analysis, including:

  1. "survival": This package contains the basic survival analysis functions such as Kaplan-Meier estimates, Cox proportional hazards model, and parametric survival models.

  2. "flexsurv": This package provides flexible parametric survival models, including Weibull, log-normal, log-logistic, and generalized gamma models.

  3. "survminer": This package contains useful functions for data visualization and risk estimation, such as survival curves, cumulative incidence curves, and hazard ratios.

  4. "rms": This package provides a wide range of regression models, including parametric survival models, and it also has functions for data visualization and model fitting.

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

# load the survival package

library(survival)

# fit a Kaplan-Meier survival estimate to the data

fit <- survfit(Surv(time, status) ~ group, data = mydata)

# plot the survival curve

plot(fit, lty = 1:2, mark.time = TRUE)

In this example, the "Surv" function is used to create a survival object with time and status as the input. The "survfit" function is then used to fit a Kaplan-Meier survival estimate to the data, and the "plot" function is used to visualize the results.