Abstract:
: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.
journal_name
J Biopharm Statjournal_title
Journal of biopharmaceutical statisticsauthors
Tango Tdoi
10.1080/10543406.2017.1293083subject
Has Abstractpub_date
2017-01-01 00:00:00pages
963-974issue
6eissn
1054-3406issn
1520-5711journal_volume
27pub_type
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