Here's my experiment: I'm testing the physiological effects of a treatment on a mice (measuring continuous variables such as total mass, fat mass, bone density, etc). I want to test the significance of this treatment on weight gain parameters.
What I've done so far: in R, I've fitted a logistic glm model, using the binary factor indicating treatment/control group as the response (I eventually want to see what weight measurements are predictive of treatment group-membership). However, I also have repeated measures at (for now) 2 time points (~3 weeks apart) and a sex variable as well. These are (uninteresting) factors effecting weight and weight gain.
My question is, how do I control for factors that I know are significantly effecting weight gain? The problem seems to be that I want to use continuous measurements to predict a classifier (treatment group) but this is probably getting swamped out by trivial attributes that are also effecting these continuous measurements (age and sex). Other than just splitting up the data into subgroups, is there are way to deal with this sort of 'mixed effect' model?
A reference would be greatly appreciated.