I have an experiment that has 2 groups to compare (treated vs control), and for each group I have 12 time points, with 7 patients per time point/group. I am wondering what is the best way to approach the following questions:
- Does treatment have an effect? If so, at which time point?
- Are there striking time effects regardless of group?
To answer these questions I've specified my design to be given by
~0 + treatment + time + patient + group:time. I have included patient ID as the same patient is sampled at multiple times, and in both treated and control situations.
Now let's say that the 12 time points are split in 2 days, day 1 and 2, but I'm only interested (for now) in answering the questions above for day 1. At a later point, I will still examine those of day 2. I can see 2 ways of proceeding:
- Model all treatments with a single design, specifying all contrasts (including any for day 2), but then extracting only the comparisons for day 1.
- Subsetting all data and contrasts to include only those in day 1, and add those for day 2 at a later point.
In approach 1, I solve multiple problems at once, but the linear model will be controlling for the effects at day 2, which is not the most pressing question right now (but I will still want to answer later). This in turn has the advantage of my being able to extract all contrasts of interest in a single pass, and it is possible that any effect in day 2 is influenced by what happened in day 1.
In approach 2, we examine everything separately. But this does not account for the possible effect that day 1 may have on the samples of day 2.
TLDR: I'm just wondering if you see any disadvantage in doing approach 1, even if I only want to examine day 1 and maybe later day 2.