Bayesian transformation model for spatial partly interval-censored data

Abstract

AbstractThe transformation model with partly interval-censored data offers a highly flexible modeling framework that can simultaneously support multiple common survival models and a wide variety of censored data types. However, the real data may contain unexplained heterogeneity that cannot be entirely explained by covariates and may be brought on by a variety of unmeasured regional characteristics. Due to this, we introduce the conditionally autoregressive prior into the transformation model with partly interval-censored data and take the spatial frailty into account. An efficient Markov chain Monte Carlo method is proposed to handle the posterior sampling and model inference. The approach is simple to use and does not include any challenging Metropolis steps owing to four-stage data augmentation. Through several simulations, the suggested method's empirical performance is assessed and then the method is used in a leukemia study.Keywords: Data augmentationMCMC methodpartly interval-censored dataspatial effectsemiparametric transformation model AcknowledgmentsThe authors wish to thank the Editor, the Associate Editor and two reviewers for their many helpful and insightful comments and suggestions.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research partially supported by the Beijing Natural Science Foundation [grant number Z210003] and National Nature Science Foundation of China [grant numbers 12171328 and 11971064].