Semiparametric and Empirical Process Methods in Oncology

描述(由申请人提供)：项目摘要：拟议项目的长期目标是为肿瘤学的临床、流行病学和基础科学研究开发灵活的半参数分析工具。半参数模型在医学研究中可能非常有用，因为它们既包括通常易于解释的参数分量，也包括允许在生物不确定性存在时具有更大灵活性的非参数分量。虽然这些模型在科学上是非常有说服力的，但针对这些模型的用户友好的推理技术的有限可用性严重限制了它们在实践中的使用。这种限制的部分原因是模型的灵活性增加了推理的难度一个数量级。解决推理方法论上的这一差距是拟议研究的中心目标。这一目标将通过实现以下四个目标来实现：(1)开发和评估用于右审查的事件间隔时间数据中的转换模型的更灵活和有效的统计分析方法；(2)开发和评估用于区间审查的癌症研究中灵活的半参数风险因素评估工具；(3)创建用于癌症研究的半参数模型的计算高效推断的工具；以及(4)为癌症研究的超高维筛查数据的分析开发灵活的半参数方法。虽然这些目标的应用领域似乎不同，但所有的目标都涉及到半参数模型推理和经验过程方法的结合。因此，这些目标的统计基础高度相关。相关性：此外，这些目标将为不同的癌症研究人员提供显著改进的具有科学意义的、灵活的和用户友好的数据分析工具集合。

DESCRIPTION (provided by applicant): Project Summary: The long-term goal of the proposed project is to develop flexible semi-parametric analysis tools for clinical, epidemiological and basic science studies in oncology. Semi-parametric models are potentially very useful in medical research because they involve both a parametric component which is usually easy to interpret and a nonparametric component which permits greater flexibility in the presence of biologic uncertainty. While these models are highly compelling scientifically, the limited availability of user-friendly inferential techniques for such models has severely restricted their use in practice. Part of the reason for this limitation is that the increased flexibility of the models adds an order of magnitude to the difficulty of the inference. Addressing this gap in inferential methodology is the central goal of the proposed research. This goal will be accomplished through achieving the following four aims: (1) Develop and evaluate more flexible and effective statistical analysis methods for transformation models in right censored time-to-event data; (2) Develop and evaluate tools for flexible semi-parametric risk-factor assessment in interval censored cancer studies; (3) Create tools for computationally efficient inference in semi-parametric models for cancer research; and (4) Develop flexible, semi-parametric methods for the analysis of extremely high dimensional screening data for cancer studies. While the application areas of these aims seems diverse, all of the aims involve a combination of semi-parametric model inference and empirical process methods. Thus the statistical underpinnings of the aims are highly interrelated. Relevance: Moreover, these aims will provide diverse cancer researchers with a significantly improved collection of scientifically meaningful, flexible, and user- friendly data analysis tools.