Abstract
In the last four decades, there has been an increasing interest in developing survival models appropriate for multiple event data and, in particular, for recurrent event data. For these situations, several extensions of the Cox’s regression model have been developed. Some of the most known models were suggested by: Prentice, Williams, and Peterson (PWP); Andersen and Gill (AG); Wei, Lin, and Weissfeld (WLW); and Lee, Wei, and Amato (LWA). These models can handle with situations where exist potentially correlated lifetimes of the same subject (due to the occurrence of more than one event for each subject) which is common in this type of data.
In this chapter we present a new model, which we call hybrid model, with the purpose of minimizing some limitations of PWP model. With this model we obtained an improvement in the precision of the parameters estimates and a better fit to the simulated data.
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Acknowledgements
This research was partially supported by FCT—Fundação para a Ciência e a Tecnologia with Portuguese Funds, Project UID/MAT/04674/2013.
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Sousa-Ferreira, I., Abreu, A.M. (2019). Hybrid Model for Recurrent Event Data. In: Ahmed, S., Carvalho, F., Puntanen, S. (eds) Matrices, Statistics and Big Data. IWMS 2016. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-17519-1_2
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