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Cited by 43Statistical Methods and Bayesian InferenceStatistical Methods in Clinical TrialsAdvanced Causal Inference Techniques

Analyzing indirect effects in cluster randomized trials. The effect of estimation method, number of groups and group sizes on accuracy and power

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Hox, Joop J., Moerbeek, Mirjam, Kluytmans, Anouck, van de Schoot, Rens·2014·Frontiers in Psychology
DOI 10.3389/fpsyg.2014.00078PMC PMC3912451
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Abstract

Cluster randomized trials assess the effect of an intervention that is carried out at the group or cluster level. Ajzen's theory of planned behavior is often used to model the effect of the intervention as an indirect effect mediated in turn by attitude, norms and behavioral intention. Structural equation modeling (SEM) is the technique of choice to estimate indirect effects and their significance. However, this is a large sample technique, and its application in a cluster randomized trial assumes a relatively large number of clusters. In practice, the number of clusters in these studies tends to be relatively small, e.g., much less than fifty. This study uses simulation methods to find the lowest number of clusters needed when multilevel SEM is used to estimate the indirect effect. Maximum likelihood estimation is compared to Bayesian analysis, with the central quality criteria being accuracy of the point estimate and the confidence interval. We also investigate the power of the test for the indirect effect. We conclude that Bayes estimation works well with much smaller cluster level sample sizes such as 20 cases than maximum likelihood estimation; although the bias is larger the coverage is much better. When only 5-10 clusters are available per treatment condition even with Bayesian estimation problems occur.

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  • Where interest lies in total effects of individual-level interventions (both direct and indirect benefits), so when contamination a positive feature of implementation, then cluster randomization might be the only design of choice.

    “On the other hand, where interest lies in total effects of individual-level interventions (both direct and indirect benefits), so when contamination a positive feature of implementation, then cluster randomization might be the only design of choice [20].”

    From Cluster randomized trials of individual-level interventions were at high risk of bias (2021) · cites this paper as [20]

    The Introduction (page unavailable) describes control-group contamination as a risk that cluster randomization can lower. It does not treat contamination as a positive feature or recommend cluster randomization to capture direct and indirect benefits from it; its stated rationale points the other way. (LLM, conf=0.82)

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Authors
Hox, J. J., Moerbeek, M., Kluytmans, A., & van de Schoot, R.
Affiliations
Utrecht University (NL)
Corresponding author
Hox, Joop J.
ORCID
Moerbeek, Mirjam
Venue
Frontiers in Psychology
Publisher
Frontiers Media SA
Published
2014
Volume
5
Pages
78-78
ISSN
1664-1078 (ISSN-L)
DOI
10.3389/fpsyg.2014.00078
PMC
PMC3912451
OpenAlex
W2114783003
Cited by
43
References
30
Citations by year
2026: 1 · 2025: 5 · 2024: 4 · 2023: 3 · 2022: 4 · 2021: 5 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 7 · 2016: 2 · 2015: 2 · 2014: 1
FWCI
4.23
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Frontiers in Psychology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
indirect effects, mediation analysis, multilevel structural equation modeling, Bayesian estimation, maximum likelihood estimation, small sample size, number of clusters, group size, statistical power, confidence interval coverage, theory of planned behavior

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