Power and sample size analysis for longitudinal mixed models of health in populations exposed to environmental contaminants: a tutorial.
Free software
General linear mixed model
Longitudinal study design
Persistent chemicals
Power analysis
Repeated measurements
Sample size
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
12 01 2023
12 01 2023
Historique:
received:
12
08
2022
accepted:
13
12
2022
entrez:
12
1
2023
pubmed:
13
1
2023
medline:
17
1
2023
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
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.
METHODS
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.
RESULTS
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.
CONCLUSIONS
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.
Identifiants
pubmed: 36635621
doi: 10.1186/s12874-022-01819-y
pii: 10.1186/s12874-022-01819-y
pmc: PMC9835314
doi:
Types de publication
Review
Journal Article
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
12Subventions
Organisme : NIEHS NIH HHS
ID : R21 ES029394
Pays : United States
Organisme : NCI NIH HHS
ID : K07 CA088811
Pays : United States
Organisme : ARRA NIH HHS
Pays : United States
Informations de copyright
© 2023. The Author(s).
Références
Environ Health. 2018 Feb 01;17(1):11
pubmed: 29391068
Commun Stat Theory Methods. 1996;25(7):
pubmed: 24363486
Environ Int. 2010 May;36(4):390-397
pubmed: 20236705
J Pediatr. 2019 Mar;206:105-112
pubmed: 30528762
Occup Environ Med. 2018 Jan;75(1):46-51
pubmed: 29133598
Sci Rep. 2016 Dec 01;6:38039
pubmed: 27905562
J Stat Softw. 2009 Apr 1;30(5):
pubmed: 25400516
Int J Hyg Environ Health. 2010 Jun;213(3):217-23
pubmed: 20488749
Am J Orthod Dentofacial Orthop. 2015 Jan;147(1):146-9
pubmed: 25533082
Environ Int. 2010 Oct;36(7):772-8
pubmed: 20579735
Int J Epidemiol. 2022 Dec 13;51(6):2000-2013
pubmed: 35679584
Psychol Sci. 2017 Nov;28(11):1547-1562
pubmed: 28902575
Am Stat. 1995 Jan 1;49(1):43-47
pubmed: 24039272
Annu Rev Med. 1968;19:283-300
pubmed: 4297619
EGEMS (Wash DC). 2017 Feb 09;4(1):1202
pubmed: 28303254
J Mol Cell Cardiol. 2019 Aug;133:217-219
pubmed: 30844362
Stat Methods Med Res. 2013 Jun;22(3):324-45
pubmed: 22491174
Am Stat. 2019;73(4):350-359
pubmed: 32042203
BMC Public Health. 2012 Oct 29;12:918
pubmed: 23107281
Int J Hyg Environ Health. 2020 Jan;223(1):256-266
pubmed: 31444118
J Agric Food Chem. 2017 Jan 25;65(3):533-543
pubmed: 28052194
J Chromatogr A. 2011 Apr 15;1218(15):2133-7
pubmed: 21084089
Stat Methods Med Res. 2011 Oct;20(5):471-87
pubmed: 20547587
Stat Med. 2011 Sep 30;30(22):2696-707
pubmed: 21751227
Stat Med. 2010 Feb 20;29(4):504-20
pubmed: 20013937
Stat Methods Med Res. 2015 Dec;24(6):1009-29
pubmed: 22357710
Stat Med. 2004 Sep 30;23(18):2799-815
pubmed: 15344187
Neurotoxicol Teratol. 1992 May-Jun;14(3):211-9
pubmed: 1386138
Psychol Methods. 2003 Dec;8(4):497-517
pubmed: 14664685
Environ Int. 2017 Sep;106:135-143
pubmed: 28645013
BMC Med Res Methodol. 2013 Jul 31;13:100
pubmed: 23902644
Environ Res. 2015 Jul;140:673-83
pubmed: 26079316
Sci Total Environ. 2018 Mar;616-617:1089-1100
pubmed: 29100694
Stat Med. 2010 Jan 30;29(2):181-92
pubmed: 19899065
Environ Health Perspect. 2007 Sep;115(9):1298-305
pubmed: 17805419
Environ Health Perspect. 2010 Feb;118(2):222-8
pubmed: 20123620
Biometrics. 1982 Dec;38(4):963-74
pubmed: 7168798
Stat Med. 2012 Jan 13;31(1):19-28
pubmed: 22162151
Nature. 2014 Jan 30;505(7485):612-3
pubmed: 24482835
Int J Epidemiol. 2020 Jun 1;49(3):979-995
pubmed: 32087011
J Stat Softw. 2013 Sep;54(10):
pubmed: 24403868
Stat Med. 2007 May 20;26(11):2297-316
pubmed: 17044139
Stat Med. 2010 Jul 30;29(17):1825-38
pubmed: 20658550
Trials. 2012 Aug 23;13:145
pubmed: 22917111
Biometrics. 1997 Sep;53(3):983-97
pubmed: 9333350
Environ Health Perspect. 2011 Jan;119(1):119-24
pubmed: 20870569
Environ Sci Technol. 2006 Jan 1;40(1):32-44
pubmed: 16433330
J Am Stat Assoc. 1992 Dec 1;87(420):1209-1226
pubmed: 24790282
Stat Med. 2000 Jul 15;19(13):1793-819
pubmed: 10861779