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Sample Size Determination for Hierarchical Longitudinal Designs with Differential Attrition Rates
Author(s) -
Roy Anindya,
Bhaumik Dulal K.,
Aryal Subhash,
Gibbons Robert D.
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00769.x
Subject(s) - sample size determination , attrition , statistics , random effects model , multilevel model , statistical power , computer science , randomization , mathematics , sampling (signal processing) , econometrics , constant (computer programming) , cluster (spacecraft) , sample (material) , randomized controlled trial , medicine , meta analysis , chemistry , surgery , dentistry , filter (signal processing) , chromatography , programming language , computer vision
Summary We consider the problem of sample size determination for three‐level mixed‐effects linear regression models for the analysis of clustered longitudinal data. Three‐level designs are used in many areas, but in particular, multicenter randomized longitudinal clinical trials in medical or health‐related research. In this case, level 1 represents measurement occasion, level 2 represents subject, and level 3 represents center. The model we consider involves random effects of the time trends at both the subject level and the center level. In the most common case, we have two random effects (constant and a single trend), at both subject and center levels. The approach presented here is general with respect to sampling proportions, number of groups, and attrition rates over time. In addition, we also develop a cost model, as an aid in selecting the most parsimonious of several possible competing models (i.e., different combinations of centers, subjects within centers, and measurement occasions). We derive sample size requirements (i.e., power characteristics) for a test of treatment‐by‐time interaction(s) for designs based on either subject‐level or cluster‐level randomization. The general methodology is illustrated using two characteristic examples.