Author

Wanfang Zhang

Date of Award

Fall 2021

Document Type

Open Access Thesis

Department

Mathematics

First Advisor

Bo Cai

Abstract

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.

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Biostatistics Commons

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