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

基本信息

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
项目类别：
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

项目摘要

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按顺序进行衡量。例如，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，癌症患者的肿瘤-淋巴结-转移阶段是一种有序尺度测量。此外，最近提倡的评估靶肿瘤病灶治疗反应的方法是实体瘤反应评估标准方法，其顺序结果定义为完全反应、部分反应、疾病稳定和疾病进展。传统的序数响应建模方法假设预测变量之间是独立的，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，这些都被违反了。最近，惩罚模型已成功应用于高通量基因组数据集，拟合线性、Logistic 和 Cox 比例风险模型，具有优异的性能。然而，惩罚模型扩展到序数响应设置尚未得到充分描述，软件也尚未普遍可用。在此，我们建议将 L1 惩罚方法应用于序数响应模型，以便在高维基因组数据构成预测变量空间时能够对常见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附加基于模型的序数分类方法来扩展我们当前的研究范围，以配合我们之前描述的基于启发式的分类树和随机森林序数方法。该应用程序的具体目标是： (1) 开发用于实现构造型 Logit 模型的 R 函数以及用于对序数响应建模的 L1 惩罚构造型 Logit 模型。 (2) 通过进行模拟研究并将模型应用于公开可用的微阵列数据集，实证检验 L1 惩罚刻板印象模型和竞争对手序数响应模型的性能。 (3) 开发一个 R 包，用于拟合聚类序数响应数据的随机效应序数回归模型。 (4) 扩展随机效应序数回归模型以包含 L1 惩罚项以适应高维协变量空间，并通过应用于微阵列数据来实证检验 L1 随机效应序数回归模型的性能。涉及同时进行组织病理学评估和微阵列研究的协议活检的研究越来越多地进行，因此本申请中开发的方法和软件将为分析此类数据提供独特的信息学方法。此外，本申请中提出的序数响应扩展虽然最初是通过考虑微阵列应用来构想的，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序数尺度上的其他响应。