PDF(Pubmed)
Abstract：
When evaluating the impact of environmental exposures on human health, study designs often include a series of repeated measurements. The goal is to determine whether populations have different trajectories of the environmental exposure over time. Power analyses for longitudinal mixed models require multiple inputs, including clinically significant differences, standard deviations, and correlations of measurements. Further, methods for power analyses of longitudinal mixed models are complex and often challenging for the non-statistician. We discuss methods for extracting clinically relevant inputs from literature, and explain how to conduct a power analysis that appropriately accounts for longitudinal repeated measures. Finally, we provide careful recommendations for describing complex power analyses in a concise and clear manner.
For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists.
As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis.
This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.
For longitudinal studies of health outcomes from environmental exposures, we show how to [1] conduct a power analysis that aligns with the planned mixed model data analysis, [2] gather the inputs required for the power analysis, and [3] conduct repeated measures power analysis with a highly-cited, validated, free, point-and-click, web-based, open source software platform which was developed specifically for scientists.
As an example, we describe the power analysis for a proposed study of repeated measures of per- and polyfluoroalkyl substances (PFAS) in human blood. We show how to align data analysis and power analysis plan to account for within-participant correlation across repeated measures. We illustrate how to perform a literature review to find inputs for the power analysis. We emphasize the need to examine the sensitivity of the power values by considering standard deviations and differences in means that are smaller and larger than the speculated, literature-based values. Finally, we provide an example power calculation and a summary checklist for describing power and sample size analysis.
This paper provides a detailed roadmap for conducting and describing power analyses for longitudinal studies of environmental exposures. It provides a template and checklist for those seeking to write power analyses for grant applications.
摘要：
背景：在评估环境暴露对人类健康的影响时，研究设计通常包括一系列重复测量。目标是确定随着时间的推移，人群是否具有不同的环境暴露轨迹。纵向混合模型的功率分析需要多个输入，包括临床上的显著差异，标准偏差，和测量的相关性。Further,纵向混合模型的幂率分析方法是复杂的，对于非统计学家来说往往具有挑战性。我们讨论了从文献中提取临床相关输入的方法，并解释如何进行功率分析，以适当地解释纵向重复措施。最后,我们提供仔细的建议，以简洁明了的方式描述复杂的功率分析。
方法：对于环境暴露对健康结果的纵向研究，我们展示了如何[1]进行与计划的混合模型数据分析相一致的动力分析，[2]收集功率分析所需的输入，和[3]进行重复测量功率分析，具有高度引用，已验证，免费,点击，基于网络的，专门为科学家开发的开源软件平台。
结果：例如，我们描述了对人体血液中全氟烷基和多氟烷基物质（PFAS）重复测量的拟议研究的功效分析。我们展示了如何调整数据分析和功耗分析计划，以考虑重复测量中的参与者内部相关性。我们说明了如何进行文献综述，以找到功率分析的输入。我们强调需要通过考虑标准偏差和平均值的差异来检查功率值的灵敏度，基于文学的价值观。最后,我们提供了一个示例功率计算和总结清单，用于描述功率和样本大小分析。
结论：本文为进行和描述环境暴露纵向研究的功率分析提供了详细的路线图。它为那些寻求为赠款应用程序编写功率分析的人提供了模板和清单。
方法：对于环境暴露对健康结果的纵向研究，我们展示了如何[1]进行与计划的混合模型数据分析相一致的动力分析，[2]收集功率分析所需的输入，和[3]进行重复测量功率分析，具有高度引用，已验证，免费,点击，基于网络的，专门为科学家开发的开源软件平台。
结果：例如，我们描述了对人体血液中全氟烷基和多氟烷基物质（PFAS）重复测量的拟议研究的功效分析。我们展示了如何调整数据分析和功耗分析计划，以考虑重复测量中的参与者内部相关性。我们说明了如何进行文献综述，以找到功率分析的输入。我们强调需要通过考虑标准偏差和平均值的差异来检查功率值的灵敏度，基于文学的价值观。最后,我们提供了一个示例功率计算和总结清单，用于描述功率和样本大小分析。
结论：本文为进行和描述环境暴露纵向研究的功率分析提供了详细的路线图。它为那些寻求为赠款应用程序编写功率分析的人提供了模板和清单。
