Hello,
I am preparing to design experiments for my research protocol and I am trying to settle on a model to analyze my data with. Below is my design setup:
Response variable: heart-rate
Random factor: subject
Fixed factor: treatment (control, treatment A, treatment B)
Goal: determine statistically significant factor/interaction effects deviating from baseline heart-rate
Protocol:
- Record baseline heart-rate for 60 minutes prior to administration of treatment
- Record heart-rate for 20 minutes during 20 minutes of treatment administration
- Record heart-rate for 60 minutes after treatment administration
I am confused because there are a variety of approaches used in the literature and there seems to be conflicting advice online regarding repeated-measures data analysis. I have read about the major benefits of using a Generalized Linear Mixed Model (GLMM) as opposed to a repeated-measures ANOVA. I am just a bit stuck with the practical application of this GLMM with data collected using my protocol.
I was thinking of averaging the recorded heart-rate into 5 minute bins, which would give me 12 baseline, 4 during treatment, and 12 post-treatment data points per subject. No real rhyme or reason why I picked 5 minutes. Most of the literature normalizes subject heart-rates by the first 5 minute bin (%) before analyzing but I am weary to do this as it does not allow for subject-to-subject comparisons of heart-rate levels.
Now, I am kind of stuck and don't know how to proceed or understand what kind of model I have.
Hoping that there might be someone out there familiar with this type of protocol that could lend a hand.
Any help is much appreciated.
Thanks,
JP
