Core 3: Biostatistics Shared Resource

本项目资料具有多变量、多源变化的特点。 EPR pO2测量结合了联合收割机在分钟尺度上肿瘤氧的纵向变化， 在高氧实验的情况下，具有以天和周为尺度的慢性变化 加上患有相同肿瘤的患者之间的差异，传感器之间pO2的差异 肿瘤内的位置以及肿瘤类型之间的变化。传统的t检验根本就不是 适用于捕捉所有这些特征，因此更复杂的统计模型， 需要技术。我们将使用混合模型理论， 随着时间的推移，随着肿瘤内和肿瘤之间的变化，对pO2进行建模， 体内氧分布的不均匀性。 在达特茅斯建立独立的生物统计学核心的理由有两个方面：（1）来自 不同的项目将在一个方法框架下进行分析，（2）达特茅斯生物统计学家 在处理多源变化非线性模式数据方面， 与癌症生物学家和临床医生合作的记录。 除其他外，以下任务将在生物统计核心进行： 和肿瘤间pO2在基线时的变化，（B）发展预测性 由于在分钟尺度上的过度氧合而导致的氧增加的模型，（c） 作为癌症后果的肿瘤氧在数天和数周过程中的纵向模式 （d）将癌症患者分类为“有反应的”或 通过统计学手段对过度氧合“无反应”。这些任务将在以下方面执行： 不同类型的肿瘤和使用不同的EPR技术，如印度墨水，氧芯片和深， 组织植入共振器。 达特茅斯的生物统计学核心将与来自所有三个研究人员密切合作 项目我们正计划定期召开会议，沿着讨论统计分析和调查结果 为临床医生和生物学家提供教育课程，解释先进的统计方法， 模型

The data to be derived in this Program Project have multivariate-multi-source variation features. The EPR pO2 measurements combine longitudinal changes of tumor oxygen on the scale of minutes, in the case of hyperoxygenation experiments, with chronic changes on the scale of days and weeks compounded with variation across patients with the same tumor, variation of pO2 across sensor locations within tumor, and variations across tumor types. The traditional t-test is simply not applicable to capture all these features and therefore more sophisticated statistical models and techniques are required. We will be using the theory of mixed models that combines nonlinear modeling of pO2 over the time with variation within and between tumors to promptly address the heterogeneity of oxygen distribution in vivo. The rationale of having a stand-alone Biostatistics Core at Dartmouth is two-fold: (1) the data from different projects will be analyzed under one methodological umbrella, (2) Dartmouth biostatisticians are the most experienced in handling multi-source variation nonlinear pattern data with a long-year track record of working with cancer biologists and clinicians. The following tasks, among others, will be performed at Biostatistics Core: (a) estimation of intra and inter tumor pO2 variation at the baseline using multisite measurements, (b) develop predictive models for increase of oxygen due to hyperoxygenation on the scale of minutes, (c) estimation of longitudinal patterns of tumor oxygen over the course of days and weeks as a consequence of cancer therapy such as radiation, surgery or chemo, (d) classification of cancer patients as "responsive" or "non-responsive" to hyperoxygenation by statistical means. These tasks will be carried out for different types of tumor and using different EPR techniques, such as india ink, OxyChip and deep- tissue implanted resonators. Biostatistics Core at Dartmouth will work with close collaboration with researchers from all three projects. We are planning to have regular meetings to discuss statistical analyses and findings along with educational sessions for clinicians and biologists to explain advanced statistical methods and models.