Recursive partitioning and ensemble methods for classifying an ordinal response

用于对序数响应进行分类的递归划分和集成方法

• 批准号: 7805045
• 负责人: Kellie J. Archer
• 金额: $ 7.5万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2010-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7805045
• 关键词: Advocate; Algorithms; Applications Grants; Area; Behavioral Research; Benchmarking; Bioconductor; Biopsy Specimen; Cancer Patient; Chronic Hepatitis; Classification; Clinical; Communities; Cox Proportional Hazards Models; Data; Data Analyses; Data Set; Drug toxicity; Economics; Education; Effectiveness; Environment; Evaluation; Faculty; Funding; Gene Expression; Genomics; Grant; Health; Health Status; Health Surveys; Hepatic; Human; In complete remission; Informatics; Lesion; Literature; Location; Logistics; Machine Learning; Mathematics; Measures; Methodology; Methods; Modeling; Neoplasm Metastasis; Occupations; Outcome; Patients; Performance; Positioning Attribute; Progressive Disease; Recommendation; Recovery; Relative (related person); Research; Research Personnel; Research Project Grants; Sample Size; Sampling; Science; Scoring Method; Simulate; Solid Neoplasm; Stable Disease; Staging; Technology; Translational Research; Travel; Trees; United States National Institutes of Health; base; computerized tools; cost; forest; heuristics; improved; indexing; interest; liver biopsy; meetings; neglect; novel; parent grant; partial response; preference; programs; research study; response; simulation; social; software development; symposium; tool; tumor

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.

描述（由申请人提供）：该提案是针对NOT-OD-09-058 NIH提交的，宣布了竞争性修订申请的恢复法案资金的可用性。健康状况和结果经常以序数量测量。例如，包括慢性肝炎患者的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，癌症患者的肿瘤淋巴结阶段是一种序数尺度。此外，最近提倡评估目标肿瘤治疗反应的方法是实体瘤方法中的反应评估标准，其顺序结局定义为完全反应，部分反应，稳定疾病和进行性疾病。传统的序数响应模型方法假定预测变量之间的独立性，并要求样本（n）的数量超过协变量（P）的数量。这些都在高通量基因组研究的背景下违反。 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）使用模拟，基准和基因表达数据集进行了与现有的名义和连续响应方法相比，评估所提出的序数分类方法； （SA.3）在预测序数响应中的利益时，开发和评估评估可变重要性的方法。最近，惩罚模型已成功地应用于具有出色性能的线性，逻辑和COX比例危害模型的高通量基因组数据集。但是，尚未描述将惩罚模型扩展到序数响应设置。本文中，我们建议将L1惩罚方法扩展到序数响应模型，以在高维基因组数据构成预测空间时启用公共顺序响应数据的建模。这项研究将通过提供适用于高维数据集的基于模型的序数分类方法来扩大我们当前研究的范围，以伴随基于启发式的分类树和父母赠款中考虑的随机森林序数方法。此竞争性修订应用程序的具体目的是：目标1）扩展L1惩罚方法，以通过为R编程环境开发计算工具来预测序数响应；目标2）使用模拟，基准和基因表达数据集，通过将我们的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序数随机森林方法进行比较，评估L1惩罚序列响应模型；目标3）评估评估来自L1惩罚序响应模型的重要协变量的方法。