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Cortesão Pais Figueira da Silva Abreu, Ana Maria
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- A review of Cox' s model extensions for multiple eventsPublication . Sousa-Ferreira, Ivo; Abreu, AnaIn longitudinal studies, it is usual that a given subject can experience several failures. To analyse multiple failure-time data, we reviewed some extensions of Cox's regression model, which were proposed by: Prentice, Williams and Peterson (PWP); Andersen and Gill (AG); Wei, Lin e Weissfeld (WLW); and Lee, Wei and Amato (LWA). Our main goal is to underline the differences between these extensions, through a brief but careful description, providing also some guidance on how to choose the proper model for each situation. The guidelines presented in this work revealed to be a useful pointer to easily choose the most suitable model. Secondarily, we used the survsim and the survival R packages to illustrate the practical implementation of these models.
- Hybrid model for recurrent event dataPublication . Sousa-Ferreira, Ivo; Abreu, Ana MariaIn the last four decades, there has been an increasing interest in developingsurvivalmodelsappropriateformultipleeventdataand,inparticular,for recurrentevent data. For these situations, several extensionsof 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);andLee,Wei, andAmato(LWA).Thesemodelscanhandlewith 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.
- Parametric regression models for recurrent events analysis based on Chen distributionPublication . Sousa-Ferreira, Ivo; Abreu, Ana Maria; Rocha, CristinaIn this paper, two parametric regression models based on Chen dis tribution are proposed for situations where recurrent events have the same or dif ferent risks of occurrence. Inference is based on a maximum likelihood approach, which ensures consistent parameter estimators. However, since in recurrent event data there is within-subject correlation, the “one step” jackknife estimator is used. An application on a real data set is also provided for illustrative purposes.