How do I normalize each of my subjects samples via their baseline level? I have 6 subjects. Each one has provided a sample every three months over a twelve month period ( 4 samples in total).
I want to account for the baseline level of each subject when I analyse the changes over time. I have been told that I need to normalise each of the subjects data points based on their baseline level, so their baseline level is ineffectively  0 when I am comparing the changes over time to this point.
Please can somebody advise how I can do this? ( Current BSc student).
Thank you
 A: To do what you are asking you simply subtract each subject's baseline measurement from all their measurements including their baseline. This will ensure that all subjects have zero baseline.
However that is a bad idea.
The main problem is that you have no way to account for possible dependency on the measurement trajectory of the baseline values. Perhaps subjects with a low baseline have a steeper trajectory. Things like that.
The first thing to do is plot your data (mesurements vs time. If you have seperate groups (eg male/female or treatment/control) then plot the groups seperately. This is to deternmine whether a linear association is plausible.
A more approprate way to model the data is a linear mixed effects model where you fit random intercepts for subjects and have time as a fixed effect. You can also allow time to vary by subject by fitting random intercepts. In R it would look something like:
model <- lmer(measure ~ time + (1|subject), data = mydata)

Ideally you would want more than 6 subects, but it's not an ideal world and 6 is a common rule of thumb to use as a minimum
