Bayesian approach for clustered interval-censored data with time-varying covariate effects

摘要

Interval-censored data arise when the failure time cannot be observed exactly but can only be determined to lie within an interval. Interval-censored data are very common in clinical trials and epidemiological studies. In this study, we consider a Bayesian approach for clustered interval-censored data under a dynamic Cox regression model. Some methods that incorporate right censoring have been developed for clustered data with temporal covariate effects. However, interval-censored data analysis under the same circumstance is much less developed. In this paper, we estimate piecewise constant coefficients based on a dynamic Cox regression model under the Bayesian framework. The dimensions of coefficients are automatically determined by the reversible jump Markov chain Monte Carlo algorithm. Meanwhile, we use a shared frailty factor for unobserved heterogeneity or for statistical dependence between observations. Simulation studies are conducted to evaluate the performance of the proposed method. The methodology is exemplified with a pediatric study on children's dental health data.