Date of Award
Open Access Thesis
In this paper, we consider the problem of multiple frailty selection for general interval-censored spatial survival data, which often occurs in clinical trials and epidemiological studies. The general interval-censored data is a mixture of left-, right- and interval-censored data. We propose a Bayesian semiparametric approach based on the Cox proportional hazard model, where monotone splines were used for non-parametrical modeling of the cumulative baseline hazards where the variable selection priors were used for frailty selection. A two-stage data augmentation with Poisson latent variables is developed for efficient computation. The approach is evaluated based a simulation study and illustrated using a set of geographically referenced smoking cessation data in Minnesota. The whole procedure is implemented in software R 4.0.4 and WinBUGS.
Zhang, W.(2021). Multiple Frailty Model for Spatially Correlated Interval-Censored. (Master's thesis). Retrieved from https://scholarcommons.sc.edu/etd/6873