Power and sample size for the S:T repeated measures design combined with a linear mixed-effects model allowing for missing data.

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 Stat

authors

Tango T

doi

10.1080/10543406.2017.1293083

subject

Has Abstract

pub_date

2017-01-01 00:00:00

pages

963-974

issue

6

eissn

1054-3406

issn

1520-5711

journal_volume

27

pub_type

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