Überlebensmessungen

Überlebensmessungen, also known as survival analysis or reliability analysis, is a statistical method used to analyze the expected duration of time until one or more events happen. These events can be the failure of a product, death in a medical study, or the occurrence of a specific outcome in a social science experiment. The core idea is to not just measure whether an event occurred, but when it occurred.

A key feature of Überlebensmessungen is its ability to handle censored data. Censoring occurs when the event of interest has not occurred for some individuals within the observation period, or when an individual is lost to follow-up. Survival analysis techniques allow for the inclusion of this incomplete information without biasing the results.

Common methods within Überlebensmessungen include the Kaplan-Meier estimator, which provides a non-parametric estimate of the survival function, and the Cox proportional hazards model, a semi-parametric model that allows for the assessment of covariates influencing survival time. These methods help researchers understand factors that affect survival and predict future outcomes. Applications are widespread, ranging from engineering and medicine to economics and sociology.