Statistical Methods for the Analysis of Recurrent Events and Event History Data

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Statistical methods based on the occurrence of a single event are not adequate for a comprehensive understanding of data producing mechanisms of event processes with complex event histories. Therefore, statistical methods and models designed for the analysis of recurrent events and other complex event histories are needed in many fields of study. Models that ignore the past of an event process are incapable of comprehensive understanding of the underlying process. Dynamic models are intensity-based stochastic process models which are used to examine the effect of past event occurrences on the present or future evolution of a process. Dynamic models include dynamic covariates, which are essentially internal time-dependent explanatory variables. The inherent nature of dynamic covariates and their complex relations with interventions, treatment effects and heterogeneity make modelling and inference challenging issues when dynamic covariates are present. Another important issue is the use of the outcome-dependent study designs in event history settings. Such designs provide a cost-effective way to analyze event history data. However, there are many methodological problems in event history settings.