Semiparametric regression modelling of current status competing risks data: a Bayesian approach

The current status censoring takes place in survival analysis when the exact event times are not known, but each individual is monitored once for their survival status. The current status data often arise in medical research, from situations that involve multiple causes of failure. Examining current status competing risks data, commonly encountered in epidemiological studies and clinical trials, is more advantageous with Bayesian methods compared to conventional approaches. They excel in integrating prior knowledge with the observed data and delivering accurate results even with small samples. Inspired by these advantages, the present study is pioneering in introducing a Bayesian framework for both modelling and analysis of current status competing risks data together with covariates. By means of the proportional hazards model, estimation procedures for the regression parameters and cumulative incidence functions are established assuming appropriate prior distributions. The posterior computation is performed using an adaptive Metropolis-Hastings algorithm. Methods for comparing and validating models have been devised. An assessment of the finite sample characteristics of the estimators is conducted through simulation studies. Through the application of this Bayesian approach to prostate cancer clinical trial data, its practical efficacy is demonstrated.