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Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

Gentry AE, Jackson-Cook CK, Lyon DE, Archer KJ - Cancer Inform (2015)

Bottom Line: Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking.We demonstrate the application of our method to predict the stage of breast cancer.The method has been made available in the ordinalgmifs package in the R programming environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

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.

No MeSH data available.


Related in: MedlinePlus

Boxplot of β-values for CpG site cgl6807687 (PCDH21), for all subjects, by stage of cancer.
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f4-cin-suppl.2-2015-201: Boxplot of β-values for CpG site cgl6807687 (PCDH21), for all subjects, by stage of cancer.

Mentions: Boxplots (Figs. 3 and 4) are shown for the two CpG sites from Table 5 with the largest absolute coefficient. The plots display the distribution of β-values for all subjects according to stage of cancer. The β-values for cg19149522 (ZDHHC4) seem to be monotonically decreasing, while the β-values for cg16807687 (PCDH21) seem to be monotonically increasing.


Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces.

Gentry AE, Jackson-Cook CK, Lyon DE, Archer KJ - Cancer Inform (2015)

Boxplot of β-values for CpG site cgl6807687 (PCDH21), for all subjects, by stage of cancer.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4447150&req=5

f4-cin-suppl.2-2015-201: Boxplot of β-values for CpG site cgl6807687 (PCDH21), for all subjects, by stage of cancer.
Mentions: Boxplots (Figs. 3 and 4) are shown for the two CpG sites from Table 5 with the largest absolute coefficient. The plots display the distribution of β-values for all subjects according to stage of cancer. The β-values for cg19149522 (ZDHHC4) seem to be monotonically decreasing, while the β-values for cg16807687 (PCDH21) seem to be monotonically increasing.

Bottom Line: Currently, statistical methods for predicting an ordinal outcome using clinical, demographic, and high-dimensional correlated features are lacking.We demonstrate the application of our method to predict the stage of breast cancer.The method has been made available in the ordinalgmifs package in the R programming environment.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA.

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.

No MeSH data available.


Related in: MedlinePlus