Bramer Lisa M, Dixon Holly M, Degnan David J, Rohlman Diana, Herbstman Julie B, Anderson Kim A, Waters Katrina M
Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland, WA 99354, United States.
Environmental and Molecular Toxicology, Oregon State University, 1007 Agriculture & Life Sciences Building, Corvallis, OR 97331, United States.
bioRxiv. 2023 Oct 2:2023.09.29.560217. doi: 10.1101/2023.09.29.560217.
Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.
可穿戴硅胶腕带是一种迅速发展的暴露评估技术，它使研究人员能够研究以前难以接触到的人群，并有潜力更全面地描绘不同社区内的化学物质暴露情况。然而，目前尚无既定的最佳实践方法来分析单个研究或多个研究中的数据，从而限制了这些数据在更大规模荟萃分析中的影响力和可获取性。我们利用来自三项研究的数据，这些数据来自纽约市和俄勒冈州尤金市的参与者佩戴的600多个腕带，呈现了一份首创的手稿，详细介绍了腕带数据的属性。我们进一步讨论并提供了常见统计建模方法中关键领域和注意事项的具体示例，在这些方法中必须确立最佳实践，以实现荟萃分析和整合来自多个研究的数据。最后，我们详细阐述了研究人员在超越专注于特定人群的单个研究的有限范围时将面临的机器学习、荟萃分析和数据整合的重要且具有挑战性的方面。
