Statistical Methods for Cancer Biomarkers

癌症生物标志物的统计方法

• 批准号: 7753158
• 负责人: Jeremy M.G. Taylor
• 金额: $ 25.27万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7753158
• 关键词: Address; Advocate; Area; Biological Markers; Blood Tests; Critical Pathways; Data; Data Set; Detection; Development; Diagnosis; Disease; Early Diagnosis; Genomics; Grant; Hybrids; Isotonic Exercise; Knowledge; Malignant Neoplasms; Measurement; Measures; Medical Research; Methodology; Methods; Modeling; Monitor; Patients; Play; Procedures; Proteomics; Research Personnel; Risk; Role; Staging; Statistical Methods; Statistical Models; Surrogate Endpoint; Symptoms; Translational Research; Work; base; disorder risk; drug development; effective therapy; improved; new technology; novel; public health relevance; randomized trial

DESCRIPTION (provided by applicant): Biomarkers play an important role in medical research, and their use is being advocated in the detection, diagnosis, staging, treatment and management of a variety of diseases, including cancer. Using novel genomic and proteomic technologies, new biomarkers are constantly being discovered. Biomarkers are viewed by some as the most critical component in moving forward translational research in cancer. They are central to the critical path, articulated by the FDA, for enhancing drug development. There are several areas where statistical consideration would enhance the utility of biomarkers. First, many researchers have suggested that a single biomarker will be insufficient for prediction and that a panel of biomarkers will be necessary. This leads to the question of how to combine the biomarkers in an "optimal" manner. Second, many of the candidate biomarkers might be used as surrogate endpoints in randomized trials. Finally, for many biomarkers assumptions of monotonicity are a-priori known, e.g. increasing levels of biomarker are associated with increased disease risk. It would be valuable to have available statistical procedures that make use of this knowledge to improve the utility of the biomarkers. In this grant, we will be developing several new statistical modeling procedures for biomarker data in cancer studies. They are summarized in the following aims: Specific Aim 1: Development of semiparametric and nonparametric multivariate isotonic regression modelling procedures for biomarkers. (a) Two-stage estimation procedures for bivariate isotonic regression models and attendant nonparametric and semiparametric profile likelihood ratio inference procedures. (b) Isotonic estimation methods with ordered categorical covariates for censored data. Specific Aim 2: Development of statistical methods for the analysis of surrogate endpoints in a single-trial and multiple-trial framework. (a) Counterfactual-based modelling approach to the analysis of surrogate endpoints. (b) Shrinkage estimation approaches to the analysis of surrogate endpoints. (c) Semi-competing risks methodology for the analysis of surrogate endpoints. Specific Aim 3: Development of hybrid model averaging methods and attendant projection-based framework for combining biomarkers to optimize predictive accuracy. PUBLIC HEALTH RELEVANCE: Statistical Methods for Cancer Biomarkers. Biomarkers are measurements that can be taken from patient, for example from a simple blood test. Biomarkers do not measure how the patients feel or symptoms they have, never-the-less they may be very useful to give early detection of a disease, or to monitor how the treatment is working, or to assess whether a new treatment if effective. The aim of this grant is to develop valid methods of analyzing datasets of biomarkers, that get the most information out of the data.

描述（由申请人提供）：生物标志物在医学研究中发挥着重要作用，它们在包括癌症在内的各种疾病的检测、诊断、分期、治疗和管理中被提倡使用。利用新的基因组学和蛋白质组学技术，新的生物标志物不断被发现。生物标志物被一些人视为推进癌症转化研究的最关键的组成部分。FDA明确表示，它们是加强药物开发的关键途径的核心。有几个领域的统计考虑将提高生物标志物的效用。首先，许多研究人员认为，单一的生物标志物不足以进行预测，一组生物标志物将是必要的。这就引出了如何以“最佳”方式组合生物标记物的问题。其次，许多候选生物标志物可能用作随机试验的替代终点。最后，对于许多生物标志物的单调性假设是先验已知的，例如，生物标志物水平的增加与疾病风险的增加有关。这将是有价值的，有可用的统计程序，利用这些知识，以提高生物标志物的效用。在这项拨款中，我们将为癌症研究中的生物标志物数据开发几种新的统计建模程序。具体目标1：发展生物标志物的半参数和非参数多元等渗回归建模程序。(a)二元等压回归模型的两阶段估计程序以及随之而来的非参数和半参数剖面似然比推断程序。(b)屏蔽数据的有序分类协变量等压估计方法。具体目标2：发展在单试验和多试验框架中替代终点分析的统计方法。(a)对替代端点的分析采用基于反事实的建模方法。(b)替代终点分析的收缩估计方法。(c)分析替代终点的半竞争风险方法。具体目标3：开发混合模型平均方法和随之而来的基于预测的框架，用于组合生物标志物以优化预测准确性。公共卫生相关性：癌症生物标志物的统计方法。生物标志物是可以从患者身上获取的测量数据，例如简单的血液测试。生物标志物不能衡量病人的感觉或症状，但它们可能对疾病的早期检测非常有用，或者监测治疗效果，或者评估一种新治疗是否有效。这项资助的目的是开发分析生物标志物数据集的有效方法，从而从数据中获得最多的信息。