Hi,
Background:
I am working with a clinical trial in which an endpoint of interest is to analyse a parameter in an organ in the body (deliberately vague language here, but hopefully clear enough to understand the statistical considerations). The trial has a parallel group design in which patients receive either active treatment or placebo. The organ in question is subdivided into 14 sections and measurement are taken for each section at baseline and post-baseline (two time points). So each patient has in total 28 observations. There is no a priori logical hierarchy between the sections. The organ is simply "sliced up" in 14 regions/sections.
Current analysis suggestion:
My current idea is to analyze this as an extension of ANCOVA, where the baseline value is added to the model as a covariate, but where the repeated measures in each patient (correlations between sections) are modeled. The proposed code is:
proc mixed data=indata;
class patient treatment section;
model post_baseline_value = treatment section baseline_value / ddfm=kenwardroger2;
lsmeans treatment / diff cl adjust=tukey ADJDFE=ROW;
repeated section / subject=patient(treatment) type=UN;
run;
An unstructured covariance structure is proposed, since there is no clear organisation/structure/hierarchy of sections. Since the analysis is akin to a "repeated measures ANCOVA", this model will use 14 observations per patient (post-baseline measurements is modeled using the baseline measurements).
Questions:
Does this model seem reasonable to the problem at hand? Are there alternative approaches I should consider? I am not entirely sure the repeated statement as defined is suitable in this context.
I can provide additional details if needed. Any input would be greatly appreciated!
Kind Regards,
JE
