Functional Methods for Radiation Exposure and Biomarker Data

描述(申请人提供)：辐射效应研究基金会(RERF)提供了非常丰富的原子弹幸存者的数据。虽然已有一些测量误差方法被应用于对RERF数据的辐射测量误差进行校正，但这些现有方法通常依赖于某些参数测量误差假设。因此，进一步开发不需要这些难以检验的参数假设的半参数或非参数方法是很重要的。剂量学数据可以被认为是未观察到的潜在辐射暴露的替代变量。生物标记物，如具有稳定染色体畸变率的细胞的百分比，可以作为未观测到的辐射剂量的一种工具变量。从大约12万名原子弹幸存者(寿命研究，LSS，队列)的确定队列中，大约4000名拥有DS02辐射剂量估计、稳定的染色体畸变数据以及心血管疾病、胃癌、肺癌或乳腺癌等疾病的结果数据的子队列将构成校准样本。通过使用校准样本的数据，我们可以估计整个LSS的辐射剂量响应，并对DS02剂量估计的不确定度进行适当调整。这里的一个重要结果是，测量误差标准差不会有假设值，而是从数据中估计，即使数据不包括重复测量或辐射剂量估计。在该提案中，感兴趣的回归问题将有一个主要队列，该队列具有所有受试者的辐射估计，但子队列中有多个与辐射相关的变量。这项建议的具体重点包括：(I)Logistic回归剂量-反应模型的方法，当暴露估计受到经典的加性误差影响，并且某些受试者的生物标记物数据可用时。(2)存活剂量-反应模型的方法，当暴露估计受制于经典的相加误差，并且某些受试者有生物标志物数据时。(3)Logistic和Cox回归剂量-反应模型的方法，当指数估计受制于Berkson和经典误差的混合，并且某些受试者有生物标志物数据时。新方法还将应用于妇女健康倡议营养生物标记物研究的双标签水数据。建议的方法将广泛应用于任何暴露-疾病关系的分析，在这种分析中，暴露测量有误差，并且校准样本有潜在的仪器变量可用。 公共卫生相关性：我们建议使用半参数或非参数方法来调整辐射剂量测量误差对估计广岛和长崎原子弹爆炸幸存者健康效应的辐射剂量反应的影响。我们提出的方法将把稳定的染色体畸变数据作为一个工具变量。拟议的方法还将应用于妇女健康倡议营养生物标记研究的双重标记水数据，并将普遍应用于任何暴露-疾病关系的分析，在这些分析中，暴露是有误差的，并且子样本有潜在的仪器变量可用。

DESCRIPTION (provided by applicant): The Radiation Effects Research Foundation (RERF) provides very rich data on the atomic bomb survivors. Although some measurement error methods have been applied to adjust for radiation measurement error for RERF data, these existing methods generally rely on certain parametric measurement error assumptions. Therefore, it is important to further develop semiparametric or nonparametric methods that do not need these parametric assumptions that are difficult to test. Dosimetry data may be considered as a surrogate variable for the unobserved underlying radiation exposure. A biomarker such as percentage of cells with stable chromosome aberrations can be treated as a type of instrumental variable for the un- observed radiation dose. From a defined cohort of about 120,000 A-bomb survivors (the Life Span Study, LSS, cohort), the subcohort of about 4,000 who have DS02 radiation dose estimates, stable chromosome aberration data, and outcome data for diseases such as cardiovascular disease, stomach cancer, lung cancer, or breast cancer, will comprise the calibration sample. By using data from the calibration sample, we can estimate radiation dose responses for the entire LSS, with an appropriate adjustment for the uncertainty in DS02 dose estimates. An important result here is that the measurement error standard deviation will not have an assumed value, but rather will be estimated from the data, even though the data do not include replicate measurements or estimates of radiation doses. In the proposal, the regression problem of interest will have a main cohort that has radiation estimation for all subjects, but multiple radiation-related variables available in a subcohort. Specific foci of this proposal include: (i) Methods for logistic regression dose-response models, when the exposure estimates are subject to classical additive errors and biomarker data are available for some subjects. (ii) Methods for survival dose-response models, when the exposure estimates are subject to classical additive errors and biomarker data are available for some subjects. (iii) Methods for logistic and Cox regression dose-response models, when the expo- sure estimates are subject to mixtures of Berkson and classical errors and biomarker data are available for some subjects. The new methods will also be applied to the doubly labeled water data from the Nutritional Biomarker Study of the Womens Health Initiative. The proposed methods will have general applications to any analysis of exposure-disease relationships in which exposures are measured with error and potential instrumental variables are available for a calibration sample. PUBLIC HEALTH RELEVANCE: We propose to use semiparametric or nonparametric methods to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses for health effects in survivors of the atomic bombings of Hiroshima and Nagasaki. Our proposed approaches will treat stable chromosome aberration data as an instrumental variable. The proposed methods will also be applied to the doubly labeled water data from the Nutritional Biomarker Study of the Womens Health Initiative, and will have general applications to any analysis of exposure-disease relationships in which exposures are measured with error and potential instrumental variables are available for a subsample.