Survival analysis toolkits in R

We’ll use two R packages for survival data analysis and visualization :

1. the survival package for survival analyses,
2. and the survminer package for ggplot2-based elegant visualization of survival analysis results

For survival analyses, the following function [in survival package] will be used:

• Surv() to create a survival object
• survfit() to fit survival curves (Kaplan-Meier estimates)
• survdiff() to perform log-rank test comparing survival curves
• coxph() to compute the Cox proportional hazards model

For the visualization, we’ll use the following function available in the survminer package:

• ggsurvplot() for visualizing survival curves
• ggcoxzph(), ggcoxdiagnostics() and ggcoxfunctional() for checking the Cox model assumptions.

These two packages can be installed as follow:

install.packages("survival")
install.packages("survminer")

Contents

Survival analysis basics: curves and logrank tests

Survival analysis

• Objectives
• Basic concepts
  • Survival time and type of events in cancer studies
  • Censoring
  • Survival and hazard functions
  • Kaplan-Meier survival estimate
• Survival analysis in R
  • Install and load required R package
  • Example data sets
  • Compute survival curves: survfit()
  • Access to the value returned by survfit()
  • Visualize survival curves
  • Kaplan-Meier life table: summary of survival curves
  • Log-Rank test comparing survival curves: survdiff()
  • Fit complex survival curves

Read more –> Survival Analysis Basics: Curves and Logrank Tests

Cox proportional hazards model

• The need for multivariate statistical modeling
• Basics of the Cox proportional hazards model
• Compute the Cox model in R
  • Install and load required R package
  • R function to compute the Cox model: coxph()
  • Example data sets
  • Compute the Cox model
  • Visualizing the estimated distribution of survival times

Read more –> Cox Proportional Hazards Model.

Cox model assumptions

• Diagnostics for the Cox model
• Assessing the validy of a Cox model in R
  • Installing and loading required R packages
  • Computing a Cox model
  • Testing proportional Hazards assumption
  • Testing influential observations
  • Testing non linearity

Read more –> Cox Model Assumptions.

Infos

This analysis has been performed using R software (ver. 3.3.2).