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 survival, OIsurv, 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).

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