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Does anyone know a method how to calculate the required sample size in a repeated measures design that will be analysed using linear mixed modelling? The design looks as follows:I have two groups (intervention/control) that are assessed at four time points (t0 before the intervention, t1 after the intervention, t2 after a three-month maintenance phase and t3 at 6-month follow-up). In the linear mixed model I will look at the interaction of time and group, allowing for random slopes and intercepts.

I will make the following assumptions:

Effect size for the comparison of groups at t2, adjusted for baseline values: d=0.45

variability of means: sd= 0.75

power: 0.8

alpha level: 0.05

Arya Vae
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  • Before you can do such analysis you need to specify how big an effect you wish to detect and the variability of the measurements. This becomes more of a problem as you add interaction terms and repeated measures over time. See [this page](https://stats.stackexchange.com/q/63391/28500) for available tools, [this page](https://stats.stackexchange.com/q/21237/28500) for an alternate approach with simulation, and [this page](https://stats.stackexchange.com/q/87695/28500) for the importance of some assumptions. You will need to provide more information about your study to get a useful answer. – EdM Feb 28 '19 at 14:46
  • check the **powerlmm** package in R: https://cran.r-project.org/package=powerlmm – Dimitris Rizopoulos Feb 28 '19 at 19:06

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