Sunday, August 2, 2015

Survival Analysis - 1

I recently was looking for methods to apply to time-to-event data and started exploring Survival Analysis Models. In this post, I'm exploring basic KM estimator. It is a nonparametric estimator of the survival function. There are couple of instances when the KM estimator comes in handy -
  • When the survival time is censored
  • Comparing survival function for different preassigned groups.

Below I'm computing KM estimator for a real dataset (on time to death for 80 males who were diagnosed with different types of tongue cancer, from package KMsurv) and a simulated dataset (created using package survsim). In addition I am using survivalOIsurv, dplyr, ggplot2 and broom for this analysis. The first example is taken from an openintro tutorial.

The rmarkdown document illustrating below analysis can also be found here. In my future posts, I'm planning to explore more on following survival models -
  • Proportional hazards model
  • Accelerated failure time Model
  • Multiple events model (More than 2 possible events)
  • Recurring events (Each subject can experience an event multiple times).


  1. This comment has been removed by a blog administrator.

  2. I really enjoyed reading your article. I found this as an informative and interesting post, so i think it is very useful and knowledgeable. I would like to thank you for the effort you have made in writing this article.

  3. COEPD LLC- Center of Excellence for Professional Development is the most trusted online training platform to global participants. We are primarily a community of Business Analysts who have taken the initiative to facilitate professionals of IT or Non IT background with the finest quality training. Our trainings are delivered through interactive mode with illustrative scenarios, activities and case studies to help learners start a successful career. We impart knowledge keeping in view of the challenging situations individuals will face in the real time, so that they can handle their job deliverables with at most confidence.