I have a simple setup. I have 10 patients and I would like to test the effect of a certain drug on kidney function. Each patient will serve as his own control and intervention.
First for the control observations, the baseline left kidney function and right kidney function are measured for each patient.
Then once the intervention is introduced, the left and right kidney function of each patient is measured again.
In order to determine the effect drug, I am looking for significant differences between the intervention and control measures.
The values in the table above are fictitious and are meant to provide clarity.
I technically have 20 paired observations, however they are not independent. There are two paired observations per patient and these are inherently correlated.
I could take the average of the left and right kidney functions, before and after the intervention. Then I would just have 10 paired observations, and I could carry out a simple paired samples t test.
Is there another way I could find significant differences without averaging or throwing away data? I would like to keep these repeated measures. Is there a way I can take their dependency into account?
I've looked up repeated measure ANOVA, but I'm not sure if that's what I need. Do I need to use a linear mixed effect model?
Note: If I do a paired t test as if they were 20 independent paired observations, my result is statistically significant p<0.02. If I average left and right and use 10 independent paired observations my result is not statistically significant, with p>0.10.