survival: Step-by-Step of Guide to Perform Survival Analysis in R

Photo by Daniel Öberg on Unsplash

Survival analysis is a statistical approach used to analyze the time until an event of interest occurs. The event could be anything that marks the endpoint of the study, such as death, relapse, failure of a system, etc. The primary goal is to estimate the time until an event happens and to understand how different factors influence the time to event.

I. Key Concepts in Survival Analysis:

1. Survival Function (S(t)):

• The probability that an event has not occurred by time t.

2. Hazard Function (h(t)):

• The instantaneous failure rate at time t, given survival up to that time.

3. Censoring:

• In many studies, some individuals may not experience the event by the end of the study. Their data is said to be censored.

4. Kaplan-Meier Estimator:

• A non-parametric method to estimate the survival function.

5. Cox Proportional-Hazards Model:

• A popular semi-parametric model that assesses the impact of covariates on survival.