Sample size determination for mediation analysis of longitudinal data
Open accessAbstract
BACKGROUND: Sample size planning for longitudinal data is crucial when designing mediation studies because sufficient statistical power is not only required in grant applications and peer-reviewed publications, but is essential to reliable research results. However, sample size determination is not straightforward for mediation analysis of longitudinal design. METHODS: To facilitate planning the sample size for longitudinal mediation studies with a multilevel mediation model, this article provides the sample size required to achieve 80% power by simulations under various sizes of the mediation effect, within-subject correlations and numbers of repeated measures. The sample size calculation is based on three commonly used mediation tests: Sobel's method, distribution of product method and the bootstrap method. RESULTS: Among the three methods of testing the mediation effects, Sobel's method required the largest sample size to achieve 80% power. Bootstrapping and the distribution of the product method performed similarly and were more powerful than Sobel's method, as reflected by the relatively smaller sample sizes. For all three methods, the sample size required to achieve 80% power depended on the value of the ICC (i.e., within-subject correlation). A larger value of ICC typically required a larger sample size to achieve 80% power. Simulation results also illustrated the advantage of the longitudinal study design. The sample size tables for most encountered scenarios in practice have also been published for convenient use. CONCLUSIONS: Extensive simulations study showed that the distribution of the product method and bootstrapping method have superior performance to the Sobel's method, but the product method was recommended to use in practice in terms of less computation time load compared to the bootstrapping method. A R package has been developed for the product method of sample size determination in mediation longitudinal study design.
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The between-group difference observed suggests that the mindfulness component of the CBT-M intervention was likely involved in the modification of depressogenic cognitions.
“Although the study sample size did not allow for mediation analyses [46], the between-group difference observed suggests that the mindfulness component of the CBT-M intervention was likely involved in the modification of depressogenic cognitions [2].”
From Online Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth With Major Depressive Disorders: Randomized Controlled Trial. (2021) · cites this paper as [46]
Citation [46] is attached to the statement that the study sample size did not allow mediation analyses, not to the subsequent mindfulness interpretation, which cites [2]. The Background says small samples may lack power to detect mediation effects, and the Results give sample-size estimates, but this paper does not assess whether the citing study’s sample was sufficient. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe model could be considered unbiased as there were no non-convergence and improper solutions problems during model estimation.
“our model could be considered unbiased as we did not face non-convergence and improper solutions problems during model estimation [39].”
From Accept anxiety to improve sleep: The impact of the covid-19 lockdown on the relationships between mindfulness, distress and sleep quality (2021) · cites this paper as [39]
The Methods section discusses estimation of multilevel mediation models, and the Results section reports simulation performance. Neither says that an absence of non-convergence or improper solutions establishes that a model is unbiased. Searches for those terms and for bias in model estimation found no passage supporting the attributed criterion. (LLM, conf=0.76)
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References 43 · Cited by 170
- Authors
- Pan, H., Liu, S., Miao, D., & Yuan, Y.
- Affiliations
- St. Jude Children's Research Hospital (US)
- The University of Texas MD Anderson Cancer Center (US)
- Air Force Medical University (CN)
- Corresponding author
- Miao, Danmin
- Yuan, Ying
- Venue
- BMC Medical Research Methodology
- Published
- 2018-03-27
- Volume
- 18
- Issue
- 1
- Pages
- 32-32
- ISSN
- 1471-2288 (ISSN-L)
- PMC
- PMC5870539
- OpenAlex
- W2795823565
- Cited by
- 170
- References
- 43
- Citations by year
- 2026: 13 · 2025: 29 · 2024: 46 · 2023: 24 · 2022: 16 · 2021: 15 · 2020: 15 · 2019: 10 · 2018: 2
- FWCI
- 8.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Research Methodology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (acceptedVersion)
- Funders
- National Cancer Institute (CA 016672, P50CA098258, CA154591, R01CA154591, P30CA016672)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Keywords
- sample size determination, mediation analysis, longitudinal data, statistical power, bootstrapping, intraclass correlation coefficient, within-subject correlation, repeated measures
- MeSH terms
- Algorithms, Computer Simulation, Humans, Longitudinal Studies, Reproducibility of Results, Models, Statistical, Sample Size, Multilevel Analysis
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