In his first video on Survival Analysis, Thomas introduced the concept of a survival function and how we can use this to compare different demographics in the case of churn.

However, an important point that was omitted here was the notion of censoring – the ability to take into account missing data, whereby the time to event is not observed. In this video, he takes a deeper dive into censoring.

He discusses the different types of censoring, looks into an example of a medical study, and shows you how this can be applied to HR.

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