Bayesian Approach for Interval-Censored Survival Data with Time-Varying Coefficients

Abstract

AbstractInterval-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 medical and epidemiological studies. In this chapter, we discuss a Bayesian approach for correlated interval-censored data under a dynamic Cox regression model. Some methods that incorporate right censoring have been developed for time-to-event data with temporal covariate effects. However, interval-censored data analysis under the same circumstance is much less developed. In this chapter, we introduce a piecewise constant coefficients estimate 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. Two illustrative examples are given to demonstrate the methods’ performance. A summary is provided to discuss the methods introduced in this chapter.