tijdtotevenementdata

Tijdtotevenementdata, often translated as time-to-event data, refers to data that measure the duration until a specified event occurs. In statistics and biostatistics, such data are used to study how long individuals remain event-free or how long it takes for an event of interest to occur, such as death, relapse, failure of a device, or onset of a disease. The analysis focuses on the timing of events and how it relates to predictors or treatments.

In a typical dataset of time-to-event data, each subject has a time T_i and a event indicator δ_i. T_i is the observed time to the event or to censoring, and δ_i equals 1 if the event was observed and 0 if the observation was censored (the event did not occur before the study ended or the subject was lost to follow-up). Censoring can be right-censoring, left-censoring, or interval censoring, with right-censoring being the most common. Competing risks and recurrent events are additional complexities in this field, where other events can preclude the primary event or events can happen multiple times.

Common analysis methods include nonparametric approaches such as the Kaplan-Meier estimator for survival functions and the log-rank test for comparing groups. Semi-parametric models like the Cox proportional hazards model estimate hazard ratios without specifying the baseline hazard. Parametric lifetime models (e.g., Weibull, exponential) assume specific distributional forms. For competing risks, methods focus on cumulative incidence functions, and for recurrent events, counting-process models may be used. Tijdtotevenementdata are derived from clinical trials, cohort studies, and registries, and they support prognosis, treatment evaluation, and comparative effectiveness research.