Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial

BMC Medical Research Methodology

Research

Kelsey E. Barton¹  John L. Adgate¹  Keith E. Muller²  Kylie K. Harrall³  Anne P. Starling⁴  Dana Dabelea⁵  Deborah H. Glueck⁶ 

[1] Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Aurora, CO, USA;Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA;Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, 80045, Aurora, CO, USA;Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA;Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, 80045, Aurora, CO, USA;Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA;Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA;Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, 80045, Aurora, CO, USA;Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA;Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA;Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado - Anschutz Medical Campus, 80045, Aurora, CO, USA;Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA;

关键词: Power analysis;    Sample size;    Persistent chemicals;    Longitudinal study design;    Repeated measurements;    General linear mixed model;    Free software;   

DOI  :  10.1186/s12874-022-01819-y

 received in 2022-08-12, accepted in 2022-12-13,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundWhen 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.MethodsFor 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.ResultsAs 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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