Statistical Methods for Cancer Biomarkers

描述(申请人提供)：生物标记物在医学研究中发挥着重要作用，正在提倡将其用于包括癌症在内的各种疾病的检测、诊断、分期、治疗和管理。利用新的基因组和蛋白质组技术，新的生物标记物不断被发现。生物标记物被一些人视为推进癌症翻译研究的最关键组成部分。它们是FDA阐明的加强药物开发的关键途径的核心。统计方面的考虑将在几个领域加强生物标记物的效用。首先，许多研究人员提出，单一的生物标记物将不足以预测，一组生物标记物将是必要的。这就引出了如何以“最佳”方式组合生物标志物的问题。其次，在随机试验中，许多候选生物标记物可能被用作替代终点。最后，对于许多生物标记物来说，单调性假设是先验已知的，例如，生物标记物水平的增加与疾病风险的增加有关。如果有可用的统计程序利用这一知识来提高生物标志物的效用，那将是很有价值的。在这笔赠款中，我们将为癌症研究中的生物标记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保序回归建模方法。(A)双变量保序回归模型的两阶段估计程序以及相应的非参数和半参数轮廓似然比推断程序。(B)截尾数据的有序分类协变量的保序估计方法。具体目标2：制定统计方法，分析单次试验和多项试验框架中的替代终点。(A)以反事实为基础的建模方法来分析代理终点。(B)用于分析替代终点的收缩估计方法。(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附带的基于投影的框架，用于组合生物标记物以优化预测准确性。公共卫生相关性：癌症生物标记物的统计方法。生物标记物是可以从患者身上获得的测量结果，例如，通过简单的血液测试。生物标记物不会衡量患者的感受或症状，但它们在及早发现疾病、监测治疗效果或评估新疗法是否有效方面可能非常有用。这笔赠款的目的是开发有效的方法来分析生物标记物的数据集，从而从数据中获得最多的信息。

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.