OBJECTIVES Increased caseload has been associated with better patient outcomes in many areas of health care, including high-risk surgery and cancer treatment. However, such a positive volume vs. outcome relationship has not yet been validated for cervical cancer brachytherapy. The purpose of this study was to examine the relationship between physician caseload and survival rates in cervical can...

This paper proposes a discrete-time hazard regression approach based on the relation between hazard rate models and excess over threshold models, which are frequently encountered in extreme value modelling. The proposed duration model employs a exible link function and incorporates the grouped-duration analogue of the wellknown Cox proportional hazards model and the proportional odds model as s...

This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors. In order to capture persistent serial dependence in the duration process, we extend the model by an o...

BACKGROUND Oral cancer leads to a considerable use of health care resources. Wide resection of the tumor and reconstruction with a pedicle flap/ free flap is widely used. This study was conducted to investigate if young age at the time of diagnosis of oral cancer requiring this treatment confers a worse prognosis. METHODS A total of 2339 patients who underwent resections for oral cancer from ...

As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...

The Cox proportional hazards model is often used to analyse time-to-event data. In many patients, the event time is unknown, either due to dropout or study end. This is known as censoring. When using the Cox proportional hazards model, it is assumed that censoring is independent of event time. However, this is an untestable, often implausible assumption; it may be that censoring is associated w...