6.16 Notes of Statistics

• It is not appropriate to use standard Bonferroni correction for the non-apriori regions, because like always, our neuroimaging dependent measures will be at least moderately intercorrelated. The standard Bonferroni assumes orthogonality (independence), which is not the case for our FA measures. It would be much better to use the modified Bonf method that I've used forever (refer to the Sankoh's paper), or FDR.
• Given there are multiple ways to modify the Bonf which seems to be a black box to me, just use FDR for now.
• I used Spearman's rho to investigate associations between smoking and drinking measures and FA in all groups. All of these associations must be adjusted for age because of the age association with FA. It does not matter if the groups are or are not different with age. We want to know if the association are significant after adjusting for the influence of age. Additionaly, lifetime years of smoking is related to age, so age absolutely must be used as a covariate. You cannot use covariates with the standard Spearman method. These analyses must be repeated with linear regression, using age as a covariate.
• In SPSS, there's something called part correlation (i.e. semi-partial correlation, different from partial correlation).
• Maybe scores of zero were assigned to non-smokers for lifetime years of smoking. If this did happen, this is a fatal design flaw. You can't assign a score of zero to someone who does not have the behavior, i.e., history of smoking. A score of zero is meaningless and creates a "zero clumping" issue that will absolutely lead to spurious results for simple correlations or linear regression.
• e.g. non-smokers can't have "0" for lifetime years of smoking, which would otherwise be equivalent to the smokers who does not have a history of smoking. This will confuse group design and mess the analysis up.