I want to assess the statistical significance between the difference in the mean of two datasets D1 and D2. However, I each data set, the measurements aren't independent: one data set is the collection of measurements done in different different individuals in different sessions. In each session one can have many different values of the same measurement. In fact is more complicated than that, because ideally I would like to detect statistical differences in a variable (lets say blood pressure along the day, for test group and placebo group).
I think that one way to see if the difference of the means of group 1 and 2 is statistically significant is to do some kind of nested bootstrap:
If there are N_I1 individuals named (1, 2, 3,...I1) in group 1 and N_I2 individuals in group 2 ,named (I1+1, I1+2, ...,I1 +I2), and each subject went to S different sessions, I have, for each bootstrap to :
-take with replacement N_I1 individuals from the pooled group(I1+I2)
-take with replacement N_I2 individuals from the pooled group(I1+I2)
-for each individual, take with replacement S measures from the pooled measures N_I1+ N_I2.
Finally see where does the difference between measures the two groups in my real data real data fits with respect to the distribution of bootstraps.
Does this makes sense ? Thanks for your patience, Im new in this forum.