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: 8733183
• 负责人: Sudipto Banerjee
• 金额: $ 0.74万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2014-09-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8733183
• 关键词: Hierarchical; Statistical; Modeling; Bayesian; Melding; 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，以融合由工作场所中测得的观察结果引起的暴露模型和现场数据。作为第一步，我们将在实验室暴露室和实际工作场所设置中构建丰富的暴露场景数据集，其中包含有关暴露决定因素的数据，例如污染物产生和通风率以及暴露率测量。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身体暴露模型和从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（即暴露量（即暴露）和投入（即，暴露量）产生可靠的统计推理（估计和预测）。我们将采用贝叶斯框架从监视数据中验证物理模型。我们的框架还将包括使用观察到的现场数据验证模型的正式统计措施。我们通过评估模型如何在监视数据中捕获特征和模式，将灵敏度分析应用于先验的选择并在一组竞争模型中选择或选择模型。我们还将开发和传播一个用户友好的统计软件包，该软件包将使职业卫生员能够为各种物理模型实施建议的方法，以无缝且方便的方式分析其数据。成功完成项目后，我们的发展将使卫生学家能够系统地评估回顾性暴露，以预测在没有工作或操作的情况下当前和将来的暴露，并仅使用少数可能具有很高可变性的空气样本来估算暴露。只有少数监视数据点，我们的贝叶斯融合框架将提供比监视更精确的暴露估计。随着计算方法和廉价软件实施的进步，我们声称将正式的建模提升到工业卫生学家军械库中必不可少的位置。