Analyzing Survival Data in R

Authors

  • Daniel LEUCUŢA „Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, Romania. Department of Medical Informatics and Biostatistics, 6 Louis Pasteur, 400349 Cluj-Napoca, Cluj.
  • Andrei ACHIMAS CADARIU „Iuliu Haţieganu” University of Medicine and Pharmacy Cluj-Napoca, Romania. Department of Medical Informatics and Biostatistics, 6 Louis Pasteur, 400349 Cluj-Napoca, Cluj.

Keywords:

R language and environment, Survival analysis.

Abstract

This paper shows how to use R language and environment for statistical computing and graphics to analyze survival data. R was chosen for this purpose because it is a powerful statistical tool, has functions that other commercial statistical software does not have, and it is also free to use. The paper presents how to organize data for survival analysis; how to import it in R; how to describe survival data; how to estimate and plot the distribution of lifetimes; how to test for differences in survival between groups; how to do semi parametric Cox proportional hazard regression; parametric exponential or Weibull regressions; and accelerated failure time regressions; how to diagnose Cox regression to check for proportional hazard assumption, to identify influential observations, and to identify nonlinearity; how to use interaction, stratification and time dependent covariates in Cox regression models in R. The paper also presents the assumptions for the methods described and some important theoretical or more practical issues linked to analysis of survival data such as censoring, or truncation.

Published

20.04.2011

How to Cite

1.
LEUCUŢA D, ACHIMAS CADARIU A. Analyzing Survival Data in R. Appl Med Inform [Internet]. 2011 Apr. 20 [cited 2024 Mar. 29];22(1, 2):62-73. Available from: https://ami.info.umfcluj.ro/index.php/AMI/article/view/120

Issue

Section

Articles