Recursive partitioning and ensemble methods for classifying an ordinal response

描述（由申请人提供）：本提案是为了响应NOT-OD-09-058 NIH宣布为竞争性修订申请提供恢复法案资金而提交的。健康状况和结果通常按顺序衡量。例如对慢性肝炎患者肝活检标本的评分方法，包括Knodell肝活动指数、Ishak评分和METAVIR评分。此外，肿瘤-淋巴结转移分期对癌症患者来说是一个有序的尺度尺度。此外，最近提倡的评估靶肿瘤病变治疗反应的方法是实体肿瘤反应评价标准（response Evaluation Criteria in Solid Tumors）方法，其顺序结果定义为完全缓解、部分缓解、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，并要求样本数量(n)超过协变量数量(p)。在高通量基因组研究的背景下，这些都是违反的。我们目前资助的R03基金“用于分类有序响应的递归划分和集成方法”包括以下三个具体目标（SA.1）扩展递归划分和随机森林分类方法，通过开发R编程环境的计算工具来预测有序响应，包括在rpart中实现我们的有序杂质标准，并在randomForest中实现有序杂质标准；（SA.2）与使用模拟、基准和基因表达数据集的现有名义和连续响应方法相比，评估拟议的顺序分类方法；（SA.3）开发和评估评估变量重要性的方法，当对预测有序反应感兴趣时。最近，惩罚模型已经成功地应用于高通量基因组数据集，用于拟合线性、逻辑和Cox比例风险模型，并取得了优异的成绩。然而，惩罚模型的扩展到有序响应设置没有被描述。在此，我们建议将L1惩罚方法扩展到有序响应模型，以便在高维基因组数据包含预测空间时能够对公共有序响应数据进行建模。本研究将通过提供一种适用于高维数据集的基于模型的有序分类方法来扩展我们当前研究的范围，以配合父资助中考虑的基于启发式分类树和随机森林有序方法。这个竞争性修订应用程序的具体目标是：目标1)扩展L1惩罚方法，通过开发R编程环境的计算工具来预测有序响应；目标2)使用模拟、基准和基因表达数据集，通过比较我们的L1拟合算法与使用前向变量选择建模策略和我们的有序随机森林方法获得的错误率，评估L1惩罚有序响应模型；目的3)评估从L1惩罚有序反应模型中评估重要协变量的方法。

DESCRIPTION (provided by applicant): This proposal is submitted in response to NOT-OD-09-058 NIH Announces the Availability of Recovery Act Funds for Competitive Revision Applications. 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. Our currently funded R03 grant, "Recursive partitioning and ensemble methods for classifying an ordinal response," consists of the following three specific aims (SA.1) extend the recursive partitioning and random forest classification methodologies for predicting an ordinal response by developing computational tools for the R programming environment including implementing our ordinal impurity criteria in rpart and implementing the ordinal impurity criteria in randomForest; (SA.2) evaluate the proposed ordinal classification methods in comparison to existing nominal and continuous response methods using simulated, benchmark, and gene expression datasets; and (SA.3) develop and evaluate methods for assessing variable importance when interest is in predicting an ordinal response. 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 described. Herein we propose to extend the L1 penalized 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 a model-based ordinal classification methodology applicable for high-dimensional datasets to accompany the heuristic based classification tree and random forest ordinal methodologies considered in the parent grant. The specific aims of this competitive revision application are to: Aim 1) Extend the L1 penalized methodology to enable predicting an ordinal response by developing computational tools for the R programming environment; Aim 2) Using simulated, benchmark, and gene expression datasets, evaluate L1 penalized ordinal response models by comparing error rates from our L1 fitting algorithm to those obtained when using a forward variable selection modeling strategy and our ordinal random forest approach; and Aim 3) Evaluate methods for assessing important covariates from L1 penalized ordinal response models.