Informatic tools for predicting an ordinal response for high-dimensional data

描述(由申请人提供)： 健康状况和结果通常是按顺序衡量的。例如，慢性肝炎患者肝活检标本的评分方法，包括Knoell肝活动指数、Ishak评分和METAVIR评分。此外，癌症患者的肿瘤转移分期是一种有序的衡量标准。此外，最近倡导的评估靶区肿瘤治疗反应的方法是实体肿瘤反应评估标准方法，其结果依次定义为完全缓解、部分缓解、稳定和进展。传统的有序响应建模方法假设预测变量之间相互独立，要求样本个数(N)超过协变量个数(P)。在高通量基因组研究的背景下，这两者都被违反了。最近，惩罚模型已经成功地应用于高通量基因组数据集，以良好的性能拟合线性、Logistic和Cox比例风险模型。然而，惩罚模型扩展到顺序响应设置的情况没有得到充分说明，也没有提供普遍可用的软件。在这里，我们建议将L1惩罚方法应用于有序响应模型，以使得当高维基因组数据包括预测空间时能够对常见的有序响应数据进行建模。这项研究将通过提供适用于高维数据集的额外的基于模型的顺序分类方法来扩展我们当前研究的范围，以配合我们先前描述的基于启发式的分类树和随机森林顺序方法。这个应用程序的具体目标是：(1)开发用于实现刻板印象Logit模型的R函数以及用于模拟顺序反应的L1惩罚刻板印象Logit模型。(2)通过模拟研究并将模型应用于公开可用的微阵列数据集，对L1惩罚刻板印象Logit模型和竞争对手顺序反应模型的性能进行了实证检验。(3)开发了一个R包，用于对聚类有序响应数据的随机效应有序回归模型进行拟合。(4)将随机效应有序回归模型扩展到包含L1惩罚项以适应高维协变量空间，并以微阵列数据为例对L1随机效应有序回归模型的性能进行了实证检验。越来越多的研究涉及同时进行组织病理学评估和微阵列研究的方案活检，因此在本申请中开发的方法和软件将为分析这种数据提供独特的信息方法。此外，本申请中提出的有序响应扩展，虽然最初是通过考虑微阵列应用来构思的，但将广泛适用于各种健康、社会和行为研究领域，这些领域通常收集有序尺度上的人类偏好数据和其他响应。

DESCRIPTION (provided by applicant): Health status and outcomes are frequently measured on an ordinal scale. Examples include scoring methods for liver biopsy specimens from patients with chronic hepatitis, including the Knodell hepatic activity index, the Ishak score, and the METAVIR score. In addition, tumor-node-metasis stage for cancer patients is an ordinal scaled measure. Moreover, the more recently advocated method for evaluating response to treatment in target tumor lesions is the Response Evaluation Criteria In Solid Tumors method, with ordinal outcomes defined as complete response, partial response, stable disease, and progressive disease. Traditional ordinal response modeling methods assume independence among the predictor variables and require that the number of samples (n) exceed the number of covariates (p). These are both violated in the context of high-throughput genomic studies. Recently, penalized models have been successfully applied to high-throughput genomic datasets in fitting linear, logistic, and Cox proportional hazards models with excellent performance. However, extension of penalized models to the ordinal response setting has not been fully described nor has software been made generally available. Herein we propose to apply the L1 penalization method to ordinal response models to enable modeling of common ordinal response data when a high-dimensional genomic data comprise the predictor space. This study will expand the scope of our current research by providing additional model-based ordinal classification methodologies applicable for high-dimensional datasets to accompany the heuristic based classification tree and random forest ordinal methodologies we have previously described. The specific aims of this application are to: (1) Develop R functions for implementing the stereotype logit model as well as an L1 penalized stereotype logit model for modeling an ordinal response. (2) Empirically examine the performance of the L1 penalized stereotype logit model and competitor ordinal response models by performing a simulation study and applying the models to publicly available microarray datasets. (3) Develop an R package for fitting a random-effects ordinal regression model for clustered ordinal response data. (4) Extend the random-effects ordinal regression model to include an L1 penalty term to accomodate high-dimensional covariate spaces and empirically examine the performance of the L1random-effects ordinal regression model through application to microarray data. Studies involving protocol biopsies where both histopathological assessment and microarray studies are performed at the same time point are increasingly being performed, so that the methodology and software developed in this application will provide unique informatic methods for analyzing such data. Moreover, the ordinal response extensions proposed in this application, though initially conceived of by considering microarray applications, will be broadly applicable to a variety of health, social, and behavioral research fields, which commonly collect human preference data and other responses on an ordinal scale.