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

用于预测高维数据顺序响应的信息工具

• 批准号: 8714054
• 负责人: Kellie J. Archer
• 金额: $ 22.7万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8714054
• 关键词: Advocate; Behavioral Research; Bioconductor; Biopsy; Biopsy Specimen; Cancer Patient; Cancer Prognosis; Categories; Chronic Hepatitis; Classification; Client satisfaction; Communities; Computer software; Cox Proportional Hazards Models; Data; Data Analyses; Data Set; Diagnostic Neoplasm Staging; Environment; Evaluation; Event; Gene Chips; Gene Expression; Genes; Genomics; Goals; Health; Health Status; Hepatic; Human; In complete remission; Informatics; Lesion; Logistics; Logit Models; Measures; Methodology; Methods; Modeling; Molecular; Nodal; Outcome; Patients; Performance; Progressive Disease; Protocols documentation; Quality of life; Recurrence; Reporting; Research; Research Personnel; Sampling; Scoring Method; Solid Neoplasm; Specimen; Stable Disease; Staging; Stereotyping; Techniques; Time; Trees; base; forest; functional status; heuristics; indexing; liver biopsy; malignant breast neoplasm; novel; partial response; preference; programs; response; simulation; social; software development; tool; tumor

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.

描述（由申请人提供）： 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。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，将惩罚模型扩展到序数响应设置尚未得到充分描述，也没有使软件通常可用。本文中，我们建议将L1惩罚方法应用于序数响应模型，以实现高维基因组数据构成预测空间时，可以对公共序数响应数据进行建模。这项研究将通过提供适用于高维数据集的其他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我们先前已经描述的随机森林序数方法。本应用程序的具体目的是：（1）开发用于实现刻板印象模型的R功能以及L1惩罚的刻板印象logit模型，用于建模序数响应。 （2）通过进行仿真研究并将模型应用于公开可用的微阵列数据集，从经验审查L1惩罚刻板印象logit模型和竞争对手序数响应模型的性能。 （3）开发一个R软件包，用于拟合随机效应的序数回归模型，以用于聚类的序数响应数据。 （4）将随机效应的顺序回归模型扩展到包括L1惩罚项，以适应高维协方差空间，并通过将L1random效应通过将微阵列数据应用于微阵列数据进行经验研究。在同一时间点进行组织病理学评估和微阵列研究的协议活检的研究正在越来越多地进行，因此本应用程序中开发的方法和软件将为分析此类数据提供独特的信息方法。此外，该应用程序中提出的序数响应扩展，尽管最初通过考虑微阵列应用来想象，但将广泛适用于各种健康，社会和行为研究领域，通常收集人类的偏好数据和其他响应，并以序数量表收集其他响应。