Recurrent breast cancer with proportional homogeneous poisson process

All cancers are usually classified further according to the extent or stage of disease so that therapies may be tailored to the particular disease stage. Moreover, detection of asymptomatic recurrences is associated with prolonged overall survival and survival from the time of initial detection of recurrence. This paper will applies Bayesian reference analysis, widely considered as the most successful method to produce objective, model-based, posterior distributions, to an inferential problem of recurrent breast cancer in survival analysis. A formulation is considered where individuals are expected to experience repeated events, along with concomitant variables. In addition, the sampling distribution of the observations is modeled through a proportional intensity homogeneous Poisson process. The medical records and pathology were accessible by the Chung Shan Medical University Hospital Tumor Registry. Finally, this study attempts to improve surveillance after treatment might lead to earlier detection of relapse, and precise assessment of recurrent status could improve outcome.