ReviewBMC medical research methodology2023
Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial.
Review in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06308081 (SMART), which is not on this map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
SMART: Social Media Anti-Vaping Messages to Reduce ENDS Use Among Sexual and Gender Minority Teens
Who cites it
8 citing papers in PubMed.
- Trajectories of post-traumatic stress disorder symptoms and risk-protective factors in recently resettled refugees in the United States.The International journal of social psychiatry · 2025Trial
- Environmental exposures and pediatric kidney health.Pediatric research · 2026Review
- Perinatal mental health and reproductive behavior among women in wartime Ukraine: a cohort study protocol.Frontiers in psychiatry · 2026Article
- Data-driven vestibular fMRI in PPPD and residual dizziness after BPPV: a narrative methodological review of AI-based phenotyping and longitudinal modeling.Frontiers in neurology · 2026Review
- Hand-in-Hand-improving occupational health and quality of care in early education and care: study protocol for a cluster-randomized controlled trial.Frontiers in public health · 2026Article
- Human-anchored longitudinal comparison of generative AI with a bias-calibrated LLM-as-judge.PloS one · 2026Article
- Using Power Analysis to Choose the Unit of Randomization, Outcome, and Approach for Subgroup Analysis for a Multilevel Randomized Controlled Clinical Trial to Reduce Disparities in Cardiovascular Health.Prevention science : the official journal of the Society for Prevention Research · 2024Article
- Stratified split sampling of electronic health records.BMC medical research methodology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.