Document Type

Article

Original Publication Date

2015

Journal/Book/Conference Title

Cancer Informatics

Volume

2015

Issue

S2

DOI of Original Publication

10.4137/CIN.S17277

Comments

Originally published at http://dx.doi.org/10.4137/CIN.S17277

Date of Submission

December 2015

Abstract

The pathological description of the stage of a tumor is an important clinical designation and is considered, like many other forms of biomedical data, an ordinal outcome. Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking. In this paper, we propose a method that fits an ordinal response model to predict an ordinal outcome for high-dimensional covariate spaces. Our method penalizes some covariates (high-throughput genomic features) without penalizing others (such as demographic and/or clinical covariates). We demonstrate the application of our method to predict the stage of breast cancer. In our model, breast cancer subtype is a nonpenalized predictor, and CpG site methylation values from the Illumina Human Methylation 450K assay are penalized predictors. The method has been made available in the ordinalgmifs package in the R programming environment.

Rights

Copyright © 2015 the author(s), publisher and licensee Libertas Academica Ltd. This is an open access article published under the Creative Commons CC-BY-NC 3.0 license.

Is Part Of

VCU Biostatistics Publications

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