Hierarchical Statistical Modeling and Bayesian Melding for Occupational Exposure

职业暴露的分层统计模型和贝叶斯融合

• 批准号: 9074848
• 负责人: Sudipto Banerjee
• 金额: $ 30.36万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9074848
• 关键词: Hierarchical; Statistical; Modeling; Bayesian; Melding

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.

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察所得的现场数据。第一步，我们将构建实验室暴露室和真实工作场所环境中暴露场景的丰富数据集，其中包含有关暴露决定因素的数据，例如污染物的产生、通风率和暴露测量。我们将开发一个全面且计算上可行的贝叶斯统计框架，用于融合职业卫生的物理暴露模型和工作场所的实验数据，以有效地解释不确定性的来源，并为系统输出（即， ，暴露）和输入（即暴露决定因素）。我们将采用贝叶斯框架来验证监测数据的物理模型。我们的框架还将包括正式的统计措施，用于通过观察到的现场数据验证模型。我们通过评估模型如何充分捕获监控数据中的特征和模式、将敏感性分析应用于先验的选择以及在一组竞争模型中选择一个模型来实现这一点。我们还将开发和传播一个用户友好的统计软件包，使职业卫生员能够对各种物理模型实施拟议的方法，以无缝和方便的方式分析他们的数据。该项目成功完成后，我们的开发将使卫生学家能够系统地评估回顾性暴露，在没有工作过程或操作的情况下预测当前和未来的暴露，并仅使用少量可能具有高变异性的空气样本来估计暴露。只需几个监测数据点，我们的贝叶斯融合框架将提供比监测更精确的暴露估计。随着计算方法的进步和廉价的软件实现，我们致力于将形式建模提升到工业卫生学家的武器库中不可或缺的地位。