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    Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes

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    Genre
    Thesis/Dissertation
    Date
    2017
    Author
    Spirko, Lauren Nicole
    Advisor
    Tang, Cheng-Yong
    Devarajan, Karthik
    Committee member
    Chitturi, Pallavi
    Dong, Yuexiao
    Obradovic, Zoran
    Department
    Statistics
    Subject
    Statistics
    Biostatistics
    Dimension Reduction
    Gene Expression
    High-dimensional Data
    Non-proportional Hazards
    Survival
    Variable Selection
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2446
    
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    DOI
    http://dx.doi.org/10.34944/dspace/2428
    Abstract
    One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes, providing insight into the disease's process. With the rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of thousands of genes and proteins resulting in enormous data sets where the number of genomic variables (covariates) is far greater than the number of subjects. It is also typical for such data sets to have a high proportion of censored observations. Methods based on univariate Cox regression are often used to select genes related to survival outcome. However, the Cox model assumes proportional hazards (PH), which is unlikely to hold for each gene. When applied to genes exhibiting some form of non-proportional hazards (NPH), these methods could lead to an under- or over-estimation of the effects. In this thesis, we develop methods that will directly address t
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