Core 3: Biostatistics Shared Resource

本计划项目中要得出的数据具有多变量-多源变化的特点。 EPR PO2测量结合了肿瘤氧气在分钟尺度上的纵向变化， 在高氧实验的情况下，在几天和几周的尺度上有慢性变化 再加上同一肿瘤患者之间的差异，传感器之间的PO2变化 肿瘤内的位置，以及不同肿瘤类型的差异。传统的t检验根本不是 适用于捕捉所有这些特征，因此更复杂的统计模型和 技术是必需的。我们将使用结合了非线性的混合模型理论 建立PO2随时间变化的模型，以及时解决肿瘤内和肿瘤之间的变化 体内氧分布的不均一性。 在达特茅斯建立一个独立的生物统计核心的理由有两个：(1)来自 不同的项目将在一个方法学的保护伞下进行分析，(2)达特茅斯生物统计学家 在处理多源变化的非线性模式数据方面经验最丰富，具有多年的 与癌症生物学家和临床医生合作的记录。 除其他外，将在生物统计核心执行以下任务：(A)估计内部 使用多部位测量的基线和肿瘤间PO2变化，(B)发展预测性 以分钟为单位的高氧增加氧气的模型，(C)估计 癌症导致的肿瘤氧气在几天和几周内的纵向模式 放射、手术或化疗等治疗；(D)将癌症患者归类为“有反应的”或 用统计学方法对高氧血症“无反应”。这些任务将在以下时间执行 不同类型的肿瘤，并使用不同的EPR技术，如印度墨水，氧芯片和深部- 植入组织的谐振器。 达特茅斯的生物统计核心公司将与这三家公司的研究人员密切合作 项目。我们计划定期举行会议，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和 模特们。

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.