**0**wrote:

Hi all,

I have 4 dose groups, technical duplicates, and pre- and post-treatment data. I'm interested in detecting a linear trend across dose groups, adjusting for pre-treatment variability. I'm not certain how to set this up. Data is like this:

`patients <- data.frame(Patient = rep(c(1,2,3,4,5,6,7,8), each=4), Treatment = factor(rep(c("Placebo", "Dose1", "Dose2", "Dose3"), each=4), levels = c("Placebo", "Dose1", "Dose2", "Dose3"), ordered=TRUE), Visit = factor(rep(c("Baseline","Baseline","Visit1","Visit1"), 8)))`

Is this sufficient?

`design <- model.matrix(~ Visit + Treatment, data=eSet)`

`corfit <- duplicateCorrelation(eSet,design,block=patients$patient)`

`fit <- lmFit(eSet, design,block=patients$patient,correlation=corfit$consensus, `

robust=TRUE)

`fit2 <- eBayes(fit)`

`topTable(fit = fit2, coef = "Treatment.L" ) `

Or does there need to be a Treatment * Visit interaction, then look at the Treatment.L * Visit1 effect?