I have a question about the random effects model in alleninf software (https://github.com/chrisfilo/alleninf). I have read the OHBM poster carefully (https://f1000research.com/posters/1097120). In the poster, it wrote that the approximate random effect analysis calculates the slope of best linear fit for each donor and performs a one sample t test on those estimates.
Now, I would like to investigate whether the significant correlation exists between statistical map and gene expression, and if so, want to use the random effects model to examine whether this correlation could generalize beyond six donors.
Here, I am just wondering that why don’t perform a one sample t test on the correlation values but on slopes. I have perform several one-sample t-test simulations using r and beta values, but obtained different results (t and p value). Actually, when beta value is large, the r value may be small, and vice versa.
In addition, if I use beta to perform one-sample t-test, I should first determine which one is dependent variable and which one is independent variable. The neuroimaging statistical map is independent and expression value is dependent variable, right?
So, which one is more appropriate to choose to perform one-sample t-test in my case?
BTW, how about the random-effect model in the NeuroVault? Use the same method as alleninf?