NDLI: Multivariate Probit Models for Interval-Censored Failure Time Data

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Multivariate Probit Models for Interval-Censored Failure Time Data

Content Provider	Semantic Scholar
Author	Zhang, Yifan
Copyright Year	2019
Abstract	Survival analysis is an important branch of statistics that analyzes the time to event data. The events of interest can be death, disease occurrence, the failure of a machine part, etc.. One important feature of this type of data is censoring: information on time to event is not observed exactly due to loss to follow-up or non-occurrence of interested event before the trial ends. Censored data are commonly observed in clinical trials and epidemiological studies, since monitoring a person’s health over time after treatment is often required in medical or health studies. In this dissertation we focus on studying multivariate interval-censored data, a special type of survival data. By saying multivariate interval-censored data, we mean that there are multiple failure time events of interest, and these failure times are known only to lie within certain intervals instead of being observed exactly. These events of interest can be associated because of sharing some common characteristics. Multivariate intervalcensored data draw more and more attention in epidemiological, social-behavioral and medical studies, in which subjects are examined multiple times and several events of interest are tested at the observation times. There are some existing methods available in literatures for analyzing multivariate interval-censored failure time data. Various models were developed for regression analysis. However, due to the complicated correlation structure between events, analyzing such type of survival data is much more difficult and new efficient methodologies are needed. Chapter 1 of this dissertation illustrates the important concepts of interval-censored data with several real data examples. A literature review of existing regression models
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=6363&context=etd
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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