Summary
In this chapter, we provided an overview of time-to-event variable analysis and how it differs from other statistical analyses that we have studied in the previous chapters. We covered censoring intuition (left, right, and interval censoring) and discussed Type I and Type II censoring. We also discussed non-informative and informative events in this chapter. We then discussed survival data and the relationship between survival and censoring times and how we record survival data with censoring. The survival function, hazard, and hazard ratio were also mentioned in the last section of this chapter.
In the next chapter, we will consider the non-parametric Kaplan-Meier model, the parametric exponential model, and also the semiparametric Cox Proportional Hazards model. We will perform real data analysis in Python by applying these models for survival analysis.
