# Chapter 14 Survival Analysis

These notes rely on the Survival Analysis in R DataCamp course, STHDA, and Applied Survival Analysis Using R (Moore 2016).

Survival analysis models time to event. Whereas linear regression outcomes are assumed to have a normal distribution, time-to-event outcomes have a Weibull or unknown distribution. Survival analysis models also deal with censoring (unknown starting event and/or ending event). These factors make survival analysis more complicated than linear regression.

Most survival analyses use the survival package for modeling and the survminer package for visualization.

library(tidyverse)
library(survival)
library(survminer)

A typical survival analysis uses Kaplan-Meier plots to visualize survival curves, log-rank tests to compare survival curves among groups, and Cox proportional hazards regression to describe the effect of variables on survival.

### References

Moore, Dirk F. 2016. Applied Survival Analysis Using R. 1st ed. New York, NY: Springer. https://eohsi.rutgers.edu/eohsi-directory/name/dirk-moore/.