A Bayesian approach for analyzing partly interval-censored data under the proportional hazards model
Chun Pan, Bo Cai, Lianming Wang
Abstract
Partly interval-censored time-to-event data often occur in biomedical studies of diseases where periodic medical examinations for symptoms of interest are necessary. Recent decades have seen blooming methods and R packages for interval-censored data; however, the research effort for partly interval-censored data is limited. We propose an efficient and easy-to-implement Bayesian semiparametric method for analyzing partly interval-censored data under the proportional hazards model. Two simulation studies are conducted to compare the performance of the proposed method with two main Bayesian methods currently available in the literature and the classic Cox proportional hazards model. The proposed method is applied to a partly interval-censored progression-free survival data from a metastatic colorectal cancer trial.