Event history analysis.

Abstract

The purpose of event history analysis is to explain why certain individuals are at a higher risk of experiencing the event(s) of interest than others. This can be accomplished by using special types of methods which, depending on the field in which they are applied, are called failure-time models, life-time models, survival models, transition-rate models, response-time models, event history models, duration models, or hazard models. Examples of textbooks discussing this class of techniques are [1], [2], [5], [7], [8], [10], and [12]. Here, we will use the terms event history, survival, and hazard models interchangeably. A hazard model is a regression model in which the “risk” of experiencing an event at a certain time point is predicted with a set of covariates. Two special features distinguish hazard models from other types of regression models. The first is that they make it possible to deal with censored observations, which contain only partial information on the timing of the event of interest. Another special feature is that covariates may change their value during the observation period. The possibility of including such timevarying covariates makes it possible to perform a truly dynamic analysis. Before discussing in more detail the most important types of hazard models, we will first introduce some basic concepts.