Power and sample size for the S:T repeated measures design combined with a linear mixed-effects model allowing for missing data.
Tango (Biostatistics 2016) proposed a new repeated measures design called the S:T repeated measures design, combined with generalized linear mixed-effects models and sample size calculations for a test of the average treatment effect that depend not only on the number of subjects but on the number of repeated measures before and after randomization per subject used for analysis. The main advantages of the proposed design combined with the generalized linear mixed-effects models are (1) it can easily handle missing data by applying the likelihood-based ignorable analyses under the missing at random assumption and (2) it may lead to a reduction in sample size compared with the simple pre-post design. In this article, we present formulas for calculating power and sample sizes for a test of the average treatment effect allowing for missing data within the framework of the S:T repeated measures design with a continuous response variable combined with a linear mixed-effects model. Examples are provided to illustrate the use of these formulas.
a Center for Medical Statistics, Minato-ku , Tokyo , Japan.
Toshiro
T
Tango
D015928:Cognitive Behavioral Therapy / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods / Q000706:statistics & numerical data*; D012107:Research Design / Q000706:statistics & numerical data; D018401:Sample Size
Likelihood-based ignorable analysis; missing at random; random intercept plus slope model