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Statistical considerations in the design and analysis of community intervention trials
Abstract
Community intervention trials are often characterized by the allocation of intact social units to different intervention groups. The assessment of adequate sample size for such trials must take into account the statistical dependencies among responses observed within an allocated unit. However, the small numbers of units typically involved in such trials imply that many methods of analysis that have been proposed for analyzing correlated data, particularly in the case of a dichotomous outcome variable, are not applicable to such designs. In this article we investigate this issue and determine the minimum number of units required per group, for the case of both a dichotomous and a continuous outcome variable, needed to provide adequate statistical power for detecting various levels of treatment effect. The use of significance testing as a method of detecting intracluster correlation is also investigated, and, in general, discouraged.
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“A sample size of 12 clusters per study arm (total number of clusters = 24) with 10 PwMS per cluster (total sample size 240) achieves 94.6% power to detect a mean difference of 9.66 between the two arms [82].”
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Metadata
- Authors
- Donner, A., & Klar, N.
- Affiliations
- Western University (CA)
- ORCID
- Klar, Neil
- Venue
- Journal of Clinical Epidemiology
- Publisher
- Elsevier BV
- Published
- 1996-04
- Volume
- 49
- Issue
- 4
- Pages
- 435-439
- ISSN
- 0895-4356 (ISSN-L)
- OpenAlex
- W2050387573
- Cited by
- 230
- References
- 38
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 10 · 2021: 6 · 2020: 10 · 2019: 7 · 2018: 13 · 2017: 16 · 2016: 8 · 2015: 12 · 2014: 8 · 2013: 8 · 2012: 9
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Clinical Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- sample size determination, statistical power, intra-cluster correlation, binary outcomes, continuous outcomes, treatment effect detection
- MeSH terms
- Smoking Prevention, Adolescent, Clinical Trials as Topic, Humans, Random Allocation, Cluster Analysis, Sample Size
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