Hi there,
I have a set of data which I want to find out what is the most suitable method for analyzing if there is a significant difference between the control and treated group (please see example data in excel attached).
Basically, I have 2 groups- control and treated, with 6 mice in each group.
One group is given a vehicle, and the other group with a test drug.
Tumor volumes are measured at specific time points.
I want to know if there is a statistical difference in tumor volume between the control and treated group. I was thinking of using repeated measures ANOVA, but from what i understand is that it is only applicable if the subjects remain the same throughout the whole study, right?
Please advise.
Thank you very much in advance!
This is a clear case for repeated measures ANOVA or a mixed effects model. Volume is the outcome, timepoint is a within-subjects predictor (which should probably be treated as continuous or ordinal, rather than nominal), and group is a between-subjects predictor. With such a small sample, though, it is very likely that you won't be able to make very meaningful inferences. Personally I think, especially for a small dataset like this, simply plotting the full dataset tells you more than a p-value does:
A mixed model is a better approach to analyze such data. Use a mixed model. You can find associations even with such a small data with a mixed model.
Yes, mixed model, but not with a normal error structure. I would assume a gamma error structure.
Model formula: Conc ~ Time + (1|Mouse)
Error: Conc|Time,Mouse ~ Gamma(a,b)
Fixed effects: Time = continuous (in days)
Random effects: Mouse = categorical (12 levels)
Thank you all for your kind responses. I will look further in those methods that all of you proposed.
Thank you so much once again
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