A Bayesian semiparametric regression model for current status data

Abstract

In survival analysis, interval censoring case I or current status censoring happens if each subject is observed only once for status of occurrence of the event of interest. Current status data often appear along with covariates in cross sectional studies and tumorigenicity studies. Cox’s proportional hazards model has been widely used to explore the relationship between lifetime variable and covariates. In this paper we propose a novel and easy to implement Bayesian approach for analyzing current status data. Under proportional hazards model, baseline survival function and regression parameters are estimated assuming proper prior distributions and implementing Metropolis Hastings algorithm for posterior computation. Methods for both model selection and model validation are suggested. Finite sample performance of the proposed method is evaluated using simulation studies. Intraocular lenses calcification data are analyzed for illustration.

Keywords:

• Bayesian inference
• Current status data
• Metropolis Hastings algorithm
• The proportional hazards model

Acknowledgements

We thank the editor and referees for their constructive comments. The first author would like to thank the Council of Scientific & Industrial Research, Government of India for financial support through the Junior Research Fellowship scheme vide No 09/0239(13499)/2022-EMR-I.

Disclosure statement

No potential conflict of interest was reported by the authors.