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Modeling Discrete Survival Time Using Genomic Feature Data
Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subseq...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Libertas Academica
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4360712/ https://www.ncbi.nlm.nih.gov/pubmed/25861216 http://dx.doi.org/10.4137/CIN.S17275 |
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author | Ferber, Kyle Archer, Kellie J |
author_facet | Ferber, Kyle Archer, Kellie J |
author_sort | Ferber, Kyle |
collection | PubMed |
description | Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment. |
format | Online Article Text |
id | pubmed-4360712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Libertas Academica |
record_format | MEDLINE/PubMed |
spelling | pubmed-43607122015-04-08 Modeling Discrete Survival Time Using Genomic Feature Data Ferber, Kyle Archer, Kellie J Cancer Inform Methodology Researchers have recently shown that penalized models perform well when applied to high-throughput genomic data. Previous researchers introduced the generalized monotone incremental forward stagewise (GMIFS) method for fitting overparameterized logistic regression models. The GMIFS method was subsequently extended by others for fitting several different logit link ordinal response models to high-throughput genomic data. In this study, we further extended the GMIFS method for ordinal response modeling using a complementary log-log link, which allows one to model discrete survival data. We applied our extension to a publicly available microarray gene expression dataset (GSE53733) with a discrete survival outcome. The dataset included 70 primary glioblastoma samples from patients of the German Glioma Network with long-, intermediate-, and short-term overall survival. We tested the performance of our method by examining the prediction accuracy of the fitted model. The method has been implemented as an addition to the ordinalgmifs package in the R programming environment. Libertas Academica 2015-03-02 /pmc/articles/PMC4360712/ /pubmed/25861216 http://dx.doi.org/10.4137/CIN.S17275 Text en © 2015 the author(s), publisher and licensee Libertas Academica Ltd. This is an open-access article distributed under the terms of the Creative Commons CC-BY-NC 3.0 License. |
spellingShingle | Methodology Ferber, Kyle Archer, Kellie J Modeling Discrete Survival Time Using Genomic Feature Data |
title | Modeling Discrete Survival Time Using Genomic Feature Data |
title_full | Modeling Discrete Survival Time Using Genomic Feature Data |
title_fullStr | Modeling Discrete Survival Time Using Genomic Feature Data |
title_full_unstemmed | Modeling Discrete Survival Time Using Genomic Feature Data |
title_short | Modeling Discrete Survival Time Using Genomic Feature Data |
title_sort | modeling discrete survival time using genomic feature data |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4360712/ https://www.ncbi.nlm.nih.gov/pubmed/25861216 http://dx.doi.org/10.4137/CIN.S17275 |
work_keys_str_mv | AT ferberkyle modelingdiscretesurvivaltimeusinggenomicfeaturedata AT archerkelliej modelingdiscretesurvivaltimeusinggenomicfeaturedata |