Kaplan Meier

Hypothesis

The Kaplan-Meier estimator relies on several hypotheses that must be verified for it to be valid. There are multiple ways to verify each of them. While the solutions to verify these assumptions range from quick approximate checks to sophisticated statistical tests, none are implemented in this library at the moment. Users must ensure these assumptions are met through manual data verification and external statistical validation.

Here, you will find an extensive list of these hypotheses along with existing solutions to test them.

Example Notebook

You’ll find here an example notebook displaying how you could use the Kaplan-Meier estimator using the SurvivalAnalysis class.

Model

The Kaplan-Meier estimator is a non-parametric statistic used to estimate the survival function from lifetime data. The formula for the survival function \(S(t)\) is defined as:

\[S(t) = \prod_{i: t_i \leq t} \left(1 - \frac{d_i}{n_i}\right)\]

Where: - \(d_i\) is the number of events (e.g., deaths) at time \(t_i\). - \(n_i\) is the number of subjects at risk (still alive and not censored) just before time \(t_i\).