Bayesian decision analysis for recurrent ovarian cancer

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 study applied Bayesian decision theorem to an inferential problem of recurrent ovarian cancer in survival analysis. A formulation is considered where individual was expected to experience repeated events, along with concomitant variables. In addition, the sampling distribution of the observations is modelled through a proportional Nonhomogeneous Poisson process. Finally, this paper develops a systematic way to integrate the expert’s opinions which will furnish clinician with valuable support for quality medical decision making.