Literature DB >> 23070588

Design effects for sample size computation in three-level designs.

Tina D Cunningham1, Robert E Johnson2.   

Abstract

Experiments with multiple nested levels where randomization can take place at any level bring challenges to the computation of sample sizes. Formulas derived under simple single-level experiments must be adjusted using multiplicative factors or design effects. In this work, we take a unified approach to finding the design effects in terms of intracluster correlations and present formulas to compute sample sizes of different levels. Equal cluster sample sizes and homogeneous within cluster variances are assumed.
© The Author(s) 2012.

Keywords:  Sample size; design effects; three-level design

Mesh:

Year:  2012        PMID: 23070588     DOI: 10.1177/0962280212460443

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  2 in total

1.  Statistical Power in Two-Level Hierarchical Linear Models with Arbitrary Number of Factor Levels.

Authors:  Yongyun Shin; Jennifer Elston Lafata; Yu Cao
Journal:  J Stat Plan Inference       Date:  2017-09-28       Impact factor: 1.111

2.  Power considerations for generalized estimating equations analyses of four-level cluster randomized trials.

Authors:  Xueqi Wang; Elizabeth L Turner; John S Preisser; Fan Li
Journal:  Biom J       Date:  2021-12-13       Impact factor: 1.715

  2 in total

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