Design effects for sample size computation in three-level designs
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 approac...
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| Veröffentlicht in: | Statistical methods in medical research 2016-04, Vol.25 (2), p.505-519 |
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| container_title | Statistical methods in medical research |
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| creator | Cunningham, Tina D Johnson, Robert E |
| description | 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. |
| doi_str_mv | 10.1177/0962280212460443 |
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| subjects | Adjustment Alcoholism - therapy Chronic Disease - prevention & control Cluster Analysis Clusters Computation Correlation Design factors Exercise Germany Health Promotion Humans Linear Models Randomization Randomized Controlled Trials as Topic Sample Size Samples Spain Statistical analysis Statistical methods Virginia |
| title | Design effects for sample size computation in three-level designs |
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