How to analyze and interpret recurrent events data in the presence of a terminal event: An application on readmission after colorectal cancer surgery

Recurrent events arise when an event occurs many times for a subject. Many models have been developed to analyze these kind of data: the Andersen-Gill's model is one of them as well as the Prentice-William and the Peterson's model, the Wei Lee and Weissfeld's model, or even frailty models, all assuming an independent and noninformative censoring. However, in practice, these assumptions may be violated by the existence of a terminal event that permanently stops the recurrent process (eg, death). Indeed, a patient who experiences an early terminal event is more likely to have a lower number of recurrent events than a patient who experiences a terminal event later. Thus, ignoring terminal events in the analysis may lead to biased results. Many methods have been developed to handle terminal events. In this paper, we describe the existing methods classifying into conditional or marginal methods and compare them in a simulation study to highlight bias in results if an inappropriate method is used, when recurrent events and terminal event are correlated. In addition, we apply the different models on a real dataset to show how results should be interpreted. Finally, we provide recommendations for choosing the appropriate method for analyzing recurrent events in the presence of a terminal event.