Statistical Methods for Environmental Data Subject to Detection Limits

受检测限影响的环境数据的统计方法

• 批准号: 9061638
• 负责人: Li Chen
• 金额: $ 7.53万
• 依托单位: UNIVERSITY OF KENTUCKY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9061638
• 关键词: Accounting; Address; Adopted; Arsenic; Attention; Cancer Control; Carpet; Case Study; Case-Control Studies; Characteristics; Chemicals; Chromium; Colon Carcinoma; Data; Data Analyses; Detection; Development; Dioxins; Dust; Environmental Carcinogens; Environmental Epidemiology; Environmental Exposure; Epidemiologic Studies; Evaluation; Exposure to; Future; Health; Interest Group; Kentucky; Lead; Literature; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Methodology; Methods; Modeling; National Health and Nutrition Examination Survey; Nickel; Non-Hodgkin' s Lymphoma; Numerical value; Occupational Epidemiology; Outcome; Pesticide Residues; Pesticides; Reporting; Research Personnel; Risk; Sampling; Statistical Methods; Structure of nail of toe; Surveys; Survival Analysis; Testing; Trace Elements; Validation; Weight; base; cancer prevention; comparison group; environment related cancer; environmental chemical; hazard; improved; innovation; insight; interdisciplinary collaboration; malignant breast neoplasm; novel; organochlorine exposure; public health relevance; simulation; statistics; success

DESCRIPTION (provided by applicant): As researchers investigate the relationship between cancer and exposure to environmental chemicals such as trace elements, pesticides, and dioxins, they often find concentrations that are lower than limits deemed reliable enough to report as numerical values. The detection limit (DL) may be a fixed number in some studies, but it can also vary widely from sample to sample in other studies. For the latter, the DL may be correlated with the exposure level, as observed in a colon cancer study in Kentucky. The data subject to DLs present challenges for data analysis and interpretation. In this proposal we focus on two important statistical problems encountered in the analysis of data from environmental epidemiologic studies: (a) estimation of the chemical distribution in a specific group; and (b) comparison of distributions among groups. For these two problems, ad hoc, parametric, and nonparametric methods have been proposed. Ad hoc methods are ill-advised unless there are relatively few measurements below DLs; and parametric methods can lead to markedly biased results when the parametric model is misspecified. Nonparametric methods have received increasing attention in recent years because of their robustness. However, current nonparametric methods simply borrow the commonly used methods for right-censored survival data, and do not take into account the following two unique characteristics of environmental exposure data with DLs: (a) it is not meaningful to define the hazard function for an exposure measurement; and (b) DL values are observable for all subjects including those whose actual exposure levels are detected. In addition, current nonparametric methods do not allow for sampling weights, which are typically present in survey data such as the National Health and Nutrition Examination Survey (NHANES). Due to these issues, current nonparametric methods may lead to the following four problems for the analysis of environmental exposure data with DLs: (a) lack of meaningful interpretation; (b) inefficient results; (c) inability to deal with the situation that the exposure level and DL are correlated; and (d) inability to handle survey data with sampling weights. To address the aforementioned problems, we will develop unified and efficient nonparametric estimation and testing methods that can (a) deal with possible correlation between the exposure level and DL; (b) incorporate sampling weights. We will utilize state-of-the-art methods for censored survival data and tailor them to environmental exposure data with DLs. The proposed methods will be applied to data from a recently conducted colon cancer case-control study in Kentucky, an ongoing lung cancer case-control study in Kentucky, and the NHANES. 1

描述（由申请人提供）：当研究人员调查癌症与环境化学物质（例如痕量元素，农药和二恶英）之间的关系时，他们经常发现浓度低于被认为可靠的限制，足以报告为数值。在某些研究中，检测极限（DL）可能是固定的数字，但是在其他研究中，它在样本中也可能有很大差异。对于后者，正如肯塔基州的一项结肠癌研究所观察到的那样，DL可能与暴露水平相关。 DLS的数据对数据分析和解释提出了挑战。在此提案中，我们关注的是在环境流行病学研究的数据分析中遇到的两个重要的统计问题：（a）特定组中化学分布的估计； （b）组之间分布的比较。对于这两个问题，已经提出了临时，参数和非参数方法。除非DLS以下相对较少的测量，否则对临时方法的建议是不明智的。当参数模型被误指定时，参数方法可能会导致明显偏差的结果。近年来，非参数方法因其稳健性而受到越来越多的关注。但是，当前的非参数方法只是借用用于右核心生存数据的常用方法，并且没有考虑到具有DLS的环境暴露数据的以下两个唯一特征：（a）定义为a危险函数定义危害功能是没有意义的。暴露测量； （b）所有受试者都可以观察到DL值，包括检测到实际暴露水平的受试者。此外，当前的非参数方法不允许进行抽样权重，这些权重通常存在于调查数据中，例如国家健康和营养检查调查（NHANES）。由于这些问题，目前的非参数方法可能会导致以下四个问题，用于分析具有DLS的环境暴露数据：（a）缺乏有意义的解释； （b）效率低下的结果； （c）无法处理 暴露水平和DL相关的情况； （d）无法处理采样权重的调查数据。为了解决上述问题，我们将开发统一，有效的非参数估计和测试方法，以（a）处理暴露水平与DL之间的可能相关性； （b）合并抽样权重。我们将利用最先进的方法来审查生存数据，并将其量身定制为使用DLS的环境暴露数据。提出的方法将应用于肯塔基州最近进行的结肠癌病例对照研究，肯塔基州正在进行的肺癌病例对照研究和NHANES的数据。 1