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.
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机译：肿瘤分期的病理学描述是重要的临床标志，与许多其他形式的生物医学数据一样，其被认为是序贯结果。当前，缺乏使用临床，人口统计学和高维相关特征来预测序数结果的统计方法。在本文中，我们提出了一种适合序数响应模型的方法来预测高维协变量空间的序数结果。我们的方法惩罚了一些协变量（高通量基因组特征），而没有惩罚其他协变量（例如人口统计学和/或临床协变量）。我们证明了我们的方法在预测乳腺癌分期中的应用。在我们的模型中，乳腺癌亚型是一个非惩罚性预测因子，而Illumina Human Methylation 450K分析法得出的CpG位点甲基化值是一个惩罚性预测因子。该方法已在R编程环境的ordinalgmifs包中提供。
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