This sounds like an easy setup, and I'm absolutely sure it's not too complicated to do, but somehow I can't figure out how to approach this.
The setting is as follows : There are 6 treatments with 16 subjects in each treatment. Weight is measured for every subject before and after treatment.
The hypothesis to be tested : is there a difference between treatments in effect (the difference in weight before and after treatment).
Very simple repeated measures design, but the thing is that the subjects are not identified. The only thing you know is that it's the same subjects, but you can't use a repeated measures due to the lack of some id variable.
Up til now, I constructed the mean difference as the difference between the means at t=0 and t=1 for every tank. Calculating the SE for each of these differences allows me to do a series of T-tests and put a Dunn-Sidak correction on it afterwards. But somehow this is cumbersome and feels not right.
Alternatively, I can substract the mean for each treatment at t=0, and do a classical one-way ANOVA. But I am not sure how I should correct the variances in order to count for the fact that I don't substract a fixed number, but a sample mean.
What is the appropriate model to use here, and if possible, where do I find that function in R?
it is NOT aov(weight~treatment*time), as this will make a comparison between eg t=0/treatment=1 and t=1/treatment=3. That's not what I want.