Kaplan-Meier Approach

The Kaplan-Meier estimator estimates the survival function from lifetime data. It is primarily used in medical research to track patient survival after treatments.

It is helpful in analyzing studies with censored data, where some patients' follow-up times end before the event of interest, typically due to death.

This estimator relies on several assumptions. First, censored patients share the same survival prospects as those continuously observed.

Secondly, survival probabilities are consistent regardless of when a subject enters the study, and finally, the event's timing is accurately recorded. In practice, monitoring events occurring between regular check-ups can be challenging.

One example involves comparing survival probabilities between two groups receiving different cancer treatments, regardless of some patients surviving by the study's end.

Key advantages of this estimator include effective handling of incomplete data and an intuitive graphical representation, which helps compare survival rates across different patient groups.

In contrast, its primary limitation is its inability to adjust for multiple risk factors or confounders, making it less effective in complex risk scenarios.