Event history analysis deals with data obtained by observing individuals over time, focusing on events occurring for the individuals under observation. Important applications are to life events of humans in demography, life insurance mathematics, epidemiology, and sociology. The basic data are the times of occurrence of the events and the types of events that occur. The standard approach to the analysis of such data is to use multistate models; a basic example is finite-state Markov processes in continuous time. Censoring and truncation are defining features of the area. This review comments specifically on three areas that are current subjects of active development, all motivated by demands from applications: sampling patterns, the possibility of causal interpretation of the analyses, and the levels and interpretation of variability.


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