Hierarchical Statistical Modeling and Bayesian Melding for Occupational Exposure

描述(由申请人提供)：我们建议开发创新的统计工具，用于融合暴露模型和在工作场所测量的观测所产生的现场数据。作为第一步，我们将构建实验室曝光室和真实工作场所环境中暴露情景的丰富数据集，其中包含污染物生成和通风率以及暴露测量等暴露决定因素的数据。我们将制定一个全面和在计算上可行的贝叶斯统计框架，将职业卫生的物理暴露模型和工作场所的实验数据结合起来，有效地解释不确定的来源，并对系统输出(即暴露)和输入(即暴露的决定因素)进行可靠的统计推断(估计和预测)。我们将使用贝叶斯框架来验证来自监测数据的物理模型。我们的框架还将包括用观测到的现场数据验证模型的正式统计措施。为此，我们评估模型如何充分地捕捉监测数据中的特征和模式，将敏感性分析应用于先验选择，并从一组竞争模型中选择或选择一个模型。我们亦会发展和发放一个方便使用的统计软件，让职业卫生人员可以为不同的物理模型实施建议的方法，以无缝和方便的方式分析他们的数据。项目成功完成后，我们的发展将使卫生人员能够系统地评估可追溯的接触情况，在没有工作过程或操作的情况下预测当前和未来的接触情况，并仅通过少量可能具有高变异性的空气样本来估计接触情况。只有几个监测数据点，我们的贝叶斯融合框架将提供比监测更准确的暴露估计。随着计算方法的进步和廉价的软件实现，我们声称要将形式建模提升到工业卫生师的武器库中不可或缺的位置。

DESCRIPTION (provided by applicant): We propose to develop innovative statistical tools for melding exposure models and field data arising from observations measured in a workplace. As a first step, we will construct a rich dataset of exposure scenarios in laboratory exposure chambers and real workplace settings, containing data on exposure determinants such as contaminant generation and ventilation rates and exposure measurements. We will develop a comprehensive and computationally feasible Bayesian statistical framework for melding the physical exposure models of occupational hygiene and experimental data from the workplace to effectively account for the sources of uncertainty and produce reliable statistical inference (estimation and predictions) for the system output (i.e., exposure) and inputs (i.e., exposure determinants). We will employ a Bayesian framework to validate physical models from monitoring data. Our framework will also include formal statistical measures for validating models with observed field data. We do so by assessing how adequately the models capture features and patterns in the monitoring data, applying sensitivity analysis to the choice of priors and choosing or selecting a model among a set of competing models. We will also develop and disseminate a user-friendly statistical software package that will enable occupational hygienists to implement the proposed methods for a wide variety of physical models to analyze their data in a seamless and convenient manner. Upon successful completion of the project, our developments will allow hygienists to systematically evaluate retrospective exposure, to predict current and future exposure in the absence of the working process or operation, and to estimate exposure with only a small number of air samples with possibly high variability. With only a few monitoring data points, our Bayesian melding framework will provide more precise estimates of exposure than monitoring. With advances in computational methods and inexpensive software implementation, we purport to exalt formal modeling to an indispensable position in the industrial hygienists' armory.