Semiparametric Regression Modeling of Current Status Competing Risks Data: A Bayesian Approach
Conference
65th ISI World Statistics Congress 2025
Format: IPS Abstract - WSC 2025
Session: IPS 802 - Regression Models for Lifetime Data
Thursday 9 October 2 p.m. - 3:40 p.m. (Europe/Amsterdam)
Abstract
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
modeling 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,
and an assessment of the finite sample characteristics of the estimators is conducted
through simulated studies. Through the application of this Bayesian approach to prostate
cancer clinical trial data, its practical efficacy is demonstrated.