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以顺序量表衡量。例如，慢性肝炎患者肝活检标本的评分方法，包括Knodell肝功能指数、Ishak评分和METAVIR评分。此外，癌症患者的肿瘤-淋巴结-转移阶段是有序标度的测量。此外，最近提倡的评价靶肿瘤病灶治疗反应的方法是实体瘤反应评价标准方法，顺序结局定义为完全反应、部分反应、疾病稳定和疾病进展。传统的有序响应建模方法假设预测变量之间的独立性，并要求样本数（n）超过协变量数（p）。在高通量基因组研究的背景下，这两个都被违反了。最近，惩罚模型已成功地应用于高通量基因组数据集的拟合线性，逻辑，和考克斯比例风险模型具有优异的性能。然而，惩罚模型扩展到有序响应设置尚未得到充分描述，也没有软件普遍可用。在这里，我们建议将L1惩罚方法应用于有序响应模型，以便在高维基因组数据包括预测空间时对常见的有序响应数据进行建模。这项研究将扩大我们目前的研究范围，提供额外的基于模型的有序分类方法适用于高维数据集，伴随着启发式的分类树和随机森林有序方法，我们以前描述。这个应用程序的具体目标是：（1）开发R函数来实现刻板印象logit模型以及L1惩罚刻板印象logit模型来建模有序反应。(2)通过进行模拟研究并将模型应用于公开的微阵列数据集，实证性地检查L1惩罚刻板印象logit模型和竞争对手有序反应模型的性能。(3)开发一个R包，用于拟合聚类有序响应数据的随机效应有序回归模型。(4)扩展的随机效应有序回归模型，包括一个L1罚项，以适应高维协变量空间，并通过应用于微阵列数据的L1随机效应有序回归模型的性能进行实证检验。越来越多地进行涉及方案活检的研究，其中在同一时间点进行组织病理学评估和微阵列研究，使得本申请中开发的方法和软件将提供用于分析此类数据的独特信息学方法。此外，本申请中提出的顺序响应扩展，虽然最初是通过考虑微阵列应用而构思的，但将广泛适用于各种健康、社会和行为研究领域，这些领域通常收集人类偏好数据和顺序尺度上的其他响应。

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.