Recursive partitioning and ensemble methods for classifying an ordinal response

描述(由申请人提供)：本提案是针对NOT-OD-09-058 NIH宣布可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。我们目前获得资助的R03基金“递归分割和集成方法用于分类有序响应”包括以下三个具体目标(SA.1)通过为R编程环境开发计算工具来扩展用于预测有序响应的递归分割和随机森林分类方法，包括在RPart中实施我们的有序杂质标准和在随机森林中实施有序杂质标准；(SA.2)使用模拟、基准和基因表达数据集与现有的名义和连续响应方法进行比较；以及(SA.3)开发和评估当感兴趣预测有序响应时用于评估变量重要性的方法。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，没有描述将惩罚模型扩展到顺序响应设置。在这里，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一种适用于高维数据集的基于模型的顺序分类方法来扩展我们目前的研究范围，以配合父母拨款中考虑的基于启发式的分类树和随机森林顺序方法。本竞争性修订申请的具体目标是：目标1)扩展L1惩罚方法，以便能够通过为R编程环境开发计算工具来预测顺序响应；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拟合算法的错误率与使用前向变量选择建模策略和我们的顺序随机森林方法获得的误差率来评估L1惩罚顺序响应模型；以及目标3)评估来自L1惩罚顺序响应模型的重要协变量的评估方法。

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.