levensdataanalyse

Levensdataanalyse, sometimes written levensdata-analyse, is a field of statistics and reliability engineering that focuses on time-to-event data. It deals with lifetimes of individuals or components, aiming to model, estimate, and compare how long events such as failures or deaths take to occur. The approach is routinely used to understand product reliability, medical survival, and maintenance planning.

Life data consist of observed times until an event occurs, and often include censoring. Right-censoring happens when the event has not yet occurred by the end of observation; left-censoring occurs when the event happened before observation began; interval-censoring occurs when the event is known to have occurred within a time interval. Truncation and sampling biases may also affect datasets. Proper handling of censoring is central to valid inferences in levensdataanalyse.

Common methods include parametric models (for example Weibull, Exponential, and Lognormal distributions) that describe the lifetime distribution; nonparametric approaches such as the Kaplan-Meier estimator and life tables; and semi-parametric models like the Cox proportional hazards model. Estimation is typically done via maximum likelihood with censoring, though Bayesian methods are also used. Model checking often involves probability plots, residual analysis, and information criteria (AIC/BIC) to compare alternative lifetimes models. Accelerated life testing is a related technique to study reliability under stress.

Applications span electronics, automotive and aerospace components, consumer products, and medical survival analysis. Levensdataanalyse provides insights for durability predictions, maintenance scheduling, warranty design, and risk assessment. It is closely related to survival analysis, with a particular emphasis on reliability and lifetime distributions. Popular software tools include R (survival, flexsurv), Python (lifelines), and various commercial packages.