Calculating power for multilevel implementation trials in mental health: Meaningful effect sizes, intraclass correlation coefficients, and proportions of variance explained by covariates Article Swipe
Nathaniel J. Williams
,
Nicholas C. Cardamone
,
Rinad S. Beidas
,
Steven C. Marcus
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/26334895241279153
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1177/26334895241279153
Background Despite the ubiquity of multilevel sampling, design, and analysis in mental health implementation trials, few resources are available that provide reference values of design parameters (e.g., effect size, intraclass correlation coefficient [ICC], and proportion of variance explained by covariates [covariate R 2 ]) needed to accurately determine sample size. The aim of this study was to provide empirical reference values for these parameters by aggregating data on implementation and clinical outcomes from multilevel implementation trials, including cluster randomized trials and individually randomized repeated measures trials, in mental health. The compendium of design parameters presented here represents plausible values that implementation scientists can use to guide sample size calculations for future trials. Method We searched NIH RePORTER for all federally funded, multilevel implementation trials addressing mental health populations and settings from 2010 to 2020. For all continuous and binary implementation and clinical outcomes included in eligible trials, we generated values of effect size, ICC, and covariate R 2 at each level via secondary analysis of trial data or via extraction of estimates from analyses in published research reports. Effect sizes were calculated as Cohen d; ICCs were generated via one-way random effects ANOVAs; covariate R 2 estimates were calculated using the reduction in variance approach. Results Seventeen trials were eligible, reporting on 53 implementation and clinical outcomes and 81 contrasts between implementation conditions. Tables of effect size, ICC, and covariate R 2 are provided to guide implementation researchers in power analyses for designing multilevel implementation trials in mental health settings, including two- and three-level cluster randomized designs and unit-randomized repeated-measures designs. Conclusions Researchers can use the empirical reference values reported in this study to develop meaningful sample size determinations for multilevel implementation trials in mental health. Discussion focuses on the application of the reference values reported in this study.
Related Topics
Concepts
Covariate
Intraclass correlation
Sample size determination
Multilevel model
Statistics
Statistical power
Random effects model
Variance (accounting)
Mental health
Clinical trial
Research design
Medicine
Econometrics
Meta-analysis
Mathematics
Psychometrics
Psychiatry
Pathology
Accounting
Business
Internal medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/26334895241279153
- OA Status
- gold
- Cited By
- 1
- References
- 75
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402932548
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402932548Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1177/26334895241279153Digital Object Identifier
- Title
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Calculating power for multilevel implementation trials in mental health: Meaningful effect sizes, intraclass correlation coefficients, and proportions of variance explained by covariatesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-01-01Full publication date if available
- Authors
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Nathaniel J. Williams, Nicholas C. Cardamone, Rinad S. Beidas, Steven C. MarcusList of authors in order
- Landing page
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https://doi.org/10.1177/26334895241279153Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1177/26334895241279153Direct OA link when available
- Concepts
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Covariate, Intraclass correlation, Sample size determination, Multilevel model, Statistics, Statistical power, Random effects model, Variance (accounting), Mental health, Clinical trial, Research design, Medicine, Econometrics, Meta-analysis, Mathematics, Psychometrics, Psychiatry, Pathology, Accounting, Business, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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