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 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.
Graduate Program in Public Health, Eastern Virginia Medical School, Norfolk, VA, USA CunninTD@evms.edu.
Tina D
TD
Cunningham
Department of Biostatistics, School of Medicine, Vanderbilt University, Nashville, TN, USA.
Robert E
RE
Johnson
D000437:Alcoholism / Q000628:therapy; D002908:Chronic Disease / Q000517:prevention & control; D016000:Cluster Analysis; D015444:Exercise; D005858:Germany; D006293:Health Promotion; D006801:Humans; D016014:Linear Models; D016032:Randomized Controlled Trials as Topic; D018401:Sample Size; D013030:Spain; D014768:Virginia