For educational purposes, a repeated measure of all participants in two conditions is modeled as linear mixed model. I’m interested in the difference in outcome while controlling for confounders c1 and c2; only fixed effects are of interest, random effects are completely negligible:
<code>model <- 'outcome ~ condition + c1 + c2 + (1 | id)'
</code>
<code>model <- 'outcome ~ condition + c1 + c2 + (1 | id)'
</code>
model <- 'outcome ~ condition + c1 + c2 + (1 | id)'
How to conduct a power analysis?
A simple, pragmatic solution would be sufficient, i.e. no sophisticated simulation based approach is needed, if a calculation with g*power or R package pwr delivers approximate results.