Bayesian Cox Models for Interval-Censored Survival Data

Content Provider	Semantic Scholar
Author	Zhang, Yue
Copyright Year	2016
Abstract	Interval-censored data arise when failure times cannot be observed exactly but can only be determined to lie within an interval. In this dissertation, our research interest focuses on correlated survival data occur when individuals under study are clustered or experience multiple events of interest. we developed three novel Bayesian Cox models to handle different types of correlated interval-censored survival data. For clustered data, we utilized a shared frailty factor for unobserved correlation between observations within the same cluster. For spatially correlated data, we first used frailty for within cluster correlation, and then assigned a conditional autoregressive distribution prior for frailties to model the spatial dependency between clusters. In the aforementioned two frailty models, we also considered time-varying coefficient for temporal covariate effect. For bivariate data, we applied copula model to account for the dependence between outcomes. Simulation studies and analysis of real data examples illustrate the performance and applications of the proposed methods.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://etd.ohiolink.edu/!etd.send_file?accession=ucin1479476510362603&disposition=attachment
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article