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Cancer has affected around eighteen million people all over the world in 2018. In Portugal, cancer was diagnosed in sixty thousand individuals during 2018, being the second leading cause of death (one in every four deaths). Following the European Directive 2011/24/EU, the Portuguese Health System has been recognizing oncology Reference Centres (RCs), which are focused on delivering best-in-class treatment for cancer patients. This paper performs a survival analysis of cancer patients in Portugal, having hospital episodes with discharge date after the official recognition, in 2016, of the first RCs for hepatobiliary, pancreatic, sarcomas and oesophageal cancer. The aim is to assess the impact of RCs on the survival probability of these patients. For each cancer type, survival curves are estimated using the Kaplan–Meier methodology, and hazard ratios are estimated for different covariates, using multivariate Extended Cox models. The results obtained support the implementation and encourage the further extension of the RC model for oncology in Portugal, as cancer patients treated in an oncology RC, overall, have a better survival probability when compared to patients who had no episode in an RC. These results are clearer for hepatobiliary and pancreatic cancer, but also visible for sarcomas and oesophageal cancer. [ABSTRACT FROM AUTHOR]

The repeated occurrence of the same event in a process is commonly observed in many domains. Such events are referred to as recurrent events. The time to occurrence of these repeated events varies from unit to unit with a possibility of events not occurring among some of the units. Invariably such data are dealt with using some of the techniques in survival analysis called recurrent event models, which are commonly encountered in epidemiological studies and clinical trials. However, it applies to other domains in science and technology. We illustrate the usefulness of recurrent event models in the context of defect proneness analysis in quality assessment of software. Some of the models in practice are introduced on data collected to study the impact of module size on defect proneness in the Mozilla product. Module size plays a significant role in defect proneness and each defect fix makes the class more susceptible to further defects. The risk estimates obtained from the different models vary owing to the differences in the properties of the models as well as the assumptions underlying it. [ABSTRACT FROM AUTHOR]